High fidelity systems, apparatus, and methods for collecting noise exposure data

ABSTRACT

Systems, apparatus, and methods for collecting, interpreting, and utilizing noise exposure data may include sensors to obtain an analog signal representative of impulse noise sound pressure and an analog signal representative of continuous noise sound pressure. At least one ADC may generate digital signals by sampling the analog signals at rates equal to or greater than twice the reciprocal of a minimum impulse noise rise time. Accelerometers may obtain data in close proximity to and remote from the sensors. At least one processor may include a first combining node to combine the digital signals to represent both the continuous noise and the impulse noise, a shock-artifact detection filter to identify a time frame including a shock artifact based on the accelerometry data, a frequency filter to generate a background-removed audio signal, an adaptive filter to estimate the shock artifact, and a second combining node to produce a shock-artifact-free audio signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of International ApplicationPCT/US2017/049919, entitled “High Fidelity Systems, Apparatus, andMethods for Collecting Noise Exposure Data” and filed Sep. 1, 2017,which in turn claims priority, under 35 U.S.C. § 119, to U.S.Application No. 62/384,409, entitled “High Fidelity Systems, Apparatus,and Methods for Collecting Noise Exposure Data” and filed Sep. 7, 2016.Each of these applications is incorporated herein by reference in itsentirety.

GOVERNMENT SUPPORT

This invention was made with government support under Contract No.FA8721-05-C-0002 awarded by the United States Air Force. The governmenthas certain rights in the invention.

TECHNICAL FIELD

The present disclosure relates generally to systems, apparatus, andmethods for collecting, interpreting, and utilizing noise exposure data.More specifically, the present disclosure relates to systems, apparatus,and methods for recording time-varying acoustic pressure, bothcontinuous noise and impulse noise.

BACKGROUND

Noise generally is classified as continuous (exhibiting only smallchanges in level over time), intermittent (interrupted by occasionalincreases in level), impulsive (containing components with sharp risesand rapid decays), or complex (a combination of the above), and thefrequency range and level can vary with the type and source of thenoise.

Noise-induced hearing loss (NIHL) is hearing loss caused by loud sounds.NIHL can be caused by a single exposure to an intense “impulse” sound,such as an explosion, or by repeated or continuous exposure to loudsounds over an extended period of time, such as noise generated in awoodworking shop. NIHL is not understood completely, but current modelsof NIHL suggest that sounds at levels above about 85 dB are likely todamage sensitive structures in the inner ear, leading to hearing loss.Current models of NIHL also suggest that extremely loud impulsive sounds(sounds with rise times shorter than about one second and peakamplitudes over about 85 dB) cause damage more quickly than softersounds with longer rise times. Loud, impulsive sounds may also causetinnitus, a condition in which the afflicted person perceives ringing inthe ears even under silent conditions.

NIHL affects up to 15% of Americans between the ages of 20 and 69, orabout 26 million people total. More than 30,000 cases of noise-inducedhearing injuries were reported among active-duty soldiers, sailors,airmen, and Marines in 2010. The number of new tinnitus incidents peryear increased 42% from 2007-2010 among service members. In 2009 alone,the government made more than 100,000 new service-connected disabilityawards for tinnitus and hearing loss. About 10% of veterans' disabilitypayments made for tinnitus and hearing loss; in 2013, hearing loss andtinnitus disability payments totaled about $850,000,000 per year.

Dosimetry involves measuring sound pressure levels (SPLs) in a noiseenvironment with the goal of estimating the total dosage to which anindividual is exposed over a period of time. Often the dose is estimatedin terms of A-weighted energy in conjunction with the equal-energyhypothesis (EEH), which assumes that accumulated noise energy issufficient to determine risk of NIHL and that the underlying temporalcharacteristics of the noise are irrelevant. Under the EEH, twoexposures are equivalent if the respective average noise levels anddurations comply with a specified exchange rate. For example, a 3-dBexchange rate often is employed such that a halving or doubling of theexposure time is accommodated with a +3 or −3 dB adjustment,respectively, to the allowable noise level.

In an effort to conserve hearing in industrial and military settings,guidelines on the maximum allowable daily noise exposure are recommendedby regulating agencies such as National Institute for OccupationalSafety and Health (NIOSH) and military branches under the U.S.Department of Defense (DoD) Hearing Conservation Program. This allowabledaily noise dosage is expressed as a percent relative to the recommendedlimit, i.e., 100% dose represents maximum allowable noise exposure foran individual. For exposure in a continuous noise environment, thecurrent military standard design criteria MIL-STD-1474E (2015) sets alimit of 85 dBA for a duration of 8 hours, where the exposure durationand level may be traded off to satisfy an equal-energy criterion using a3 dB exchange rate.

SUMMARY

Noise dosimeters may be used to measure noise exposure and report thedosage accumulated over the course of a day, work shift or event ofinterest. The challenges of accurate noise dosimetry are due to a numberof factors, including the variety of noise types and environmentsencountered, and the demands this variety places on dosimeters and theiruse. Typical commercial noise dosimeters record only peak noise levelsand average noise levels over a given sampling period (usually a minute)and fail to retain any spectral information about the recorded sounds.

Typical commercial noise dosimeters are required to operate only up to140 dB sound-pressure level (SPL) and cover a frequency range similar tothat of human hearing. However, weapons fire, blasts, and other impactnoises can exceed this SPL limit, and impulses can exhibit acousticbandwidths extending well beyond the audio spectrum due to their shortdurations. Thus, dosimeter design, for example with respect tomicrophone and analog-to-digital converter performance, is critical formeasurement success. Dosimeter placement also can affect measuredresults, as free-field, on-body, and in/near-ear microphone positionscan yield variations in measured spectra and levels due to absorption byclothing, head shadowing, and pinna resonances.

Unfortunately, little is known about exposure to impulsive sounds, muchless the noise-induced injury mechanisms associated with impulsivesounds. To address this dearth of information about injuries caused byimpulsive sounds, the inventors have developed technology to provideaudio recordings with broader bandwidths and larger peak amplitudes thanconventional noise dosimeters. A high sampling rate, broad spectrumnoise dosimeter can record sample rates up to 200 kHz (stereo) tocapture fast rise times of impulse noise, over a broad measurement rangeof SPL (e.g., about 50 dB SPL to about 185 dB SPL). However, until now,no system has existed to measure full spectrum high sampling rate noisehistories with a small, lightweight, and lower power device. While fixeddosimetry “stations” may be practical to set up and maintain,spatially-varying noise fields and moving subjects may requireindividually-worn dosimeters to assess personal noise exposureaccurately. Particular strengths of some embodiments include a smallform factor, high sampling rate, and simplicity. Thus, some embodimentsexpand the capabilities of existing noise recorders and dosimeters andenable measurements in contexts and environments in which other deviceswould likely be impaired or broken.

More specifically, some embodiments include a compact, portable packagesuitable for acquiring data continuously in particularly ruggedenvironments, such as battlefields, for several hours at a time. Thedata collected by some embodiments may be used to more preciselyestimate the sound exposure experienced by the user and to create moreprecise models for predicting NIHL. This data also may be used todevelop more advanced mitigation techniques, including active hearingprotection.

Embodiments of the present invention may include a portable system andcorresponding methods for recording sound in an environment subject toimpulse noise characterized by an initial rise time, which may be about50 μs or less. Some examples of the portable system comprise a firstmicrophone, a second microphone, a combining/summing node coupled to thefirst and second microphones, an analog-to-digital converter (ADC)coupled to the combining/summing node, and a processor coupled to theADC. In operation, the first microphone, which may be worn on an articleof clothing, a hat, a helmet, or a bag, produces a first analog signalrepresentative of sound in a first amplitude range, and the secondmicrophone produces a second analog signal representative of sound in asecond amplitude range different than the first amplitude range (e.g.,higher or lower than the first amplitude range). The combining/summingnode combines the first analog signal and the second analog signal intoa combined analog signal with a combined amplitude range that is aboutequal to the sum of the first amplitude range and the second amplituderange. The ADC samples the combined analog signal at a sampling rate(e.g., about 20 kHz to about 200 kHz) that is equal to or greater thantwice the reciprocal of the initial rise time so as to produce a digitalsignal representative of the combined analog signal. And the processorstores a representation of the digital signal in a nonvolatile memory.

In some cases, the first amplitude range extends from about 115 dB toabout 180 dB, the second amplitude range extends from about 75 dB toabout 140 dB, and the combined amplitude range extends from about 75 dBto about 180 dB. The portable system may also include an attenuator thatis operably coupled to an output of the first microphone and a firstinput of the combining/summing node in order to attenuate the firstanalog signal, and an amplifier that is operably coupled to an output ofthe second microphone and a second input of the combining/summing nodein order to amplify the second analog signal.

In certain examples, the processor is configured to identify at leastone portion of the digital signal corresponding to at least a portion ofthe impulse noise. The processor may also (i) divide the digital signalinto a plurality of time-frequency bins, (ii) estimate an amount ofenergy in each time-frequency bin in the plurality of time-frequencybins to produce a plurality of energy estimates, and (iii) store theplurality of energy estimates in the nonvolatile memory as at least aportion of the representation of the digital signal. Such a processormay also select the distribution and/or sizes of the time-frequency binsso as to non-invertibly blur speech content information in therepresentation of the digital signal. It may also select thedistribution and/or sizes of the time-frequency bins so as tosubstantially preserve spectral and intensity information of thecombined analog signal in the representation of the digital signal.

The portable system may also include a first buffer that is operablycoupled to the ADC. In operation, the first buffer stores at least onefirst sample of the analog signal generated by the ADC. In theseexamples, the processor may transfer the first sample from the firstbuffer to the nonvolatile memory and interrupt the transfer to store atleast one second sample of the analog signal generated by the ADC in asecond buffer operably coupled to the ADC. The processor may alsointerrupt the transfer based on acquisition of the second sample by theADC or a signal from a timer.

The portable system may also include a power supply, which is coupled toADC and the processor, to supply electrical power to the ADC and theprocessor. In some examples, the portable system also includes a housingdisposed at least partially about the combining/summing node, the ADC,the processor, and/or the power supply. It may also include acommunications interface, operably coupled to the processor, to transmitthe representation of the digital signal to an electronic device.

Other embodiments of the present invention include a portable system fordigitizing and recording an analog signal representative of at least onemeasurement of an environment. This portable system may include an ADC,a first buffer, and a processor. In operation, the ADC generates atleast one first sample of the analog signal at a sample rate of about 20kHz to about 200 kHz. The first buffer, which is operably coupled to theADC, stores at least one first sample of the analog signal generated bythe ADC. And the processor, which is operably coupled to the ADC and thefirst buffer, transfers the first sample from the first buffer to anonvolatile memory so as to store a digital representation of the analogsignal in the nonvolatile memory. The processor also interrupts thetransfer to store at least one second sample of the analog signalgenerated by the ADC in a second buffer operably coupled to the ADC,e.g., in response to a signal from a timer or the ADC's acquisition of asecond signal.

Some examples of this embodiment also include at least one microphone,operably coupled to the ADC, to provide the analog signal representativeof the measurement of the environment. These examples may include anarray of microphones to provide an analog signal comprising a pluralityof audio tracks.

Yet another embodiment of the present invention includes a method ofrecording sound in an environment subject to impulse noise characterizedby a rise time less than or equal to about 50 μs. This method involvesproducing, with a microphone, an analog signal representative of theimpulse noise, the analog signal having a bandwidth of at least about 15kHz and a peak amplitude of at least about 180 dB. An ADC generates afirst sample of the analog signal at a sampling rate equal to or greaterthan 40 kHz. This first sample is stored in a buffer, then written fromthe buffer to a non-transitory memory in a period less than or equal toabout 25 μs. And the ADC generates a second sample of the analog at thesampling rate.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIG. 1 is a table illustrating noise types and example waveforms inaccordance with some embodiments.

FIG. 2A is a block diagram illustrating a compact, portable audiorecording system suitable for recording high-impulse sounds with asingle microphone in accordance with some embodiments.

FIG. 2B is a block diagram illustrating the compact, portable audiorecording system of FIG. 2A with a single microphone using balanceddetection for extended dynamic range in accordance with someembodiments.

FIG. 2C is a block diagram illustrating the compact, portable audiorecording system of FIG. 2A with balanced stereo microphones inaccordance with some embodiments.

FIG. 2D is a block diagram illustrating the compact, portable audiorecording system of FIG. 2A with an array of microphones to provide ananalog signal with multiple audio tracks in accordance with someembodiments.

FIG. 3 is a flow diagram illustrating interrupt-driven processing basedon a timing signal employed by a compact, portable audio recordingsystem in accordance with some embodiments.

FIG. 4A is a spectrogram of frequency versus time and a plot ofamplitude versus time illustrating a recording of automatic rifle firecaptured using a high-bandwidth audio recording system in accordancewith some embodiments. FIG. 4B is a magnified view, corresponding to asingle gunshot, of the spectrogram of frequency versus time and the plotof amplitude versus time in FIG. 4A in accordance with some embodiments.

FIG. 5 is a diagram illustrating signal processing for removing speechor other information while preserving impulse information from thesignal captured by a high-bandwidth audio recording system in accordancewith some embodiments.

FIG. 6A is a spectrogram of frequency versus time of a single spokensentence recorded by a high-bandwidth audio recording system inaccordance with some embodiments. FIG. 6B is a spectrogram of frequencyversus time of the single spoken sentence in FIG. 6A after speechremoval according to the process illustrated in FIG. 5 in accordancewith some embodiments.

FIG. 7A is a plot of amplitude versus time and a spectrogram offrequency versus time of an original speech waveform with formant tracksand a pitch track recorded by a high-bandwidth audio recording system inaccordance with some embodiments. FIG. 7B is a plot amplitude versustime and a spectrogram of frequency versus time of a re-synthesizedspeech waveform with formant tracks and a pitch track generated from theoriginal speech waveform depicted in FIG. 7A in accordance with someembodiments.

FIG. 8 is a block diagram illustrating the accelerometer false impulserejection model in accordance with some embodiments.

FIG. 9A is a block diagram illustrating a channel combination algorithmto extend dynamic range in accordance with some embodiments. FIG. 9B isa series of plots illustrating an implementation of the full algorithmfor an impulsive noise source, with an attenuation and gain channel onthe same pressure sensor, in accordance with some embodiments.

FIG. 10A is a display illustrating a map with symbols indicatinglocations corresponding to noise exposure events and a user positionrelative to those locations in accordance with some embodiments. FIG.10B is a display illustrating a map with symbols indicating a locationcorresponding to a noise exposure event based on positions of multipleusers relative to the location in accordance with some embodiments.

FIG. 11 is a diagram illustrating a micro-dosimeter for attachment to orimplantation in a subject in accordance with some embodiments.

FIG. 12 is an image illustrating a first-generation prototype dosimetersystem in accordance with some embodiments.

FIG. 13 is a diagram illustrating a dosimeter system in accordance withsome embodiments.

FIG. 14 is a diagram illustrating dosimeter system components inaccordance with some embodiments.

FIG. 15 is an image illustrating a second-generation prototype dosimetersystem in accordance with some embodiments.

FIG. 16 is a series of plots illustrating pressure waveforms and⅓-octave-band spectra collected with the second-generation prototypedosimeter system in accordance with some embodiments.

FIG. 17 is a diagram illustrating a signal processing and data storagealgorithm for a complex-noise, tactical noise dosimeter in accordancewith some embodiments.

FIG. 18 is a series of simultaneous recordings illustrating live weaponsfire as observed by two individuals fielding on-body dosimeters inaccordance with some embodiments.

FIG. 19 is a plot illustrating 24-hour dosimeter collection on anaircraft carrier in accordance with some embodiments.

FIG. 20 is a scatter plot illustrating damage risk metrics calculatedfrom the 24-hour dosimeter collection on the aircraft carrier inaccordance with some embodiments.

FIG. 21 is a diagram illustrating how dosimeter collection is affectedby microphone placement in accordance with some embodiments.

FIG. 22 is a series of plots comparing peak SPL and AHAAH model at fourrecording locations for impulse noise in accordance with someembodiments.

DETAILED DESCRIPTION

The present disclosure relates generally to systems, apparatus, andmethods for recording and/or measuring time-varying acoustic pressure,including continuous noise and/or impulse noise, while retainingspectral and/or temporal information about the signal, such as risetimes, peak amplitudes, and/or energy across frequency bands. Noiseexposure data may be used to influence hearing protection standards,development, and training. Noise exposure data is of particular interestto government and military agencies, which use the data to betterunderstand the effects of complex noise environments on soldiers (amongothers). However, access to a full spectrum and dynamic range of noiseexposure histories also is of interest to consumer, medical, andoccupational health industries.

Examples of the present technology include compact, portable systemssuitable for recording broadband audio signals, even in rugged andhostile environments. In some cases, these systems may be small andlight enough to be worn on headgear or clothing, yet store several hoursof audio data acquired at bandwidths of about 10 kHz to about 100 kHz ormore. The audio data collected by these systems can be used to measure awide variety of different noise parameters, including parameters relatedto exposure to impulsive sounds (e.g., gunshots), for use in improvingpredictive modeling of potential noise-induced hearing injuries. Forexample, a system may be used to quantify a soldier's exposure to noiseon the battlefield, a construction worker's exposure to noise at aconstruction site, or a factory worker's exposure to noise in a factoryby recording real-time acoustic source characterization of backgroundand impulsive sounds. Other applications include characterizing sound(s)associated with raceways (e.g., at motor sports events), spacecraftlaunches, pyrotechnics, logging operations, sawmills, lumberyards,demolition, construction, gun ranges, subways, trains, airplanes andairports, and emergencies (e.g., fires, shootings), etc.

The collected data can be used to develop a “transfer function” thatmaps environmental noise dosimetry data to standard auditory damagemodels. The collected data also may be used to investigate soundpropagation from the body to the tympanic membrane and to assessstandard auditory damage models. Sound exposure information derived fromthe collected data can be used to modify noise-induced hearing injurymodels in order to improve the ability to predict auditory damage anddesign optimized mitigation strategies.

To understand the needs of a dosimetry device, it can be informative toclassify noise into three general types: continuous, intermittent, andimpulsive, as illustrated in the table of FIG. 1, including examplewaveforms of each type. Of these types, impulsive noise may be the mostdemanding with respect to dosimeter design due to its highly dynamicnature and extreme levels, for example, from weapons fire. Thischallenging range of conditions drive the need for a broadband dosimetrydevice with a high sampling rate and a wide dynamic range to avoidclipping or distortion from large blasts. A fourth type, complex noise,combines background (continuous or intermittent) and impulsive noise,each independently contributing sufficiently high levels to inducetemporary threshold shifts (TTS).

In practice, dosimetry data can be collected with free-field, on-body,or in-ear devices. Free-field noise surveys typically are short induration (lasting no more than a few hours) and characterize the noiselevels of an environment rather than for an individual. Accuratelytranslating a free-field survey to the dose for an individual can bechallenging. For example, reverberating noise within closed spaces canproduce spatially varying noise levels where the perceived level at theeardrum can vary dramatically (10 dB or more) depending on the exactpositioning of body and ear relative to the noise source. Thisvariability is particularly problematic for impulsive noise, due in partto its broad spectrum. High frequency (short duration, short wavelength)components are susceptible to reflections from shorter spatial scales,resulting in reverberation and multipath that can dynamically change thenoise levels observed as an individual moves or interacts with his orher environment. As a result, routine motion could result in fluctuatingnoise levels throughout the day. In an attempt to address this, manymodern, small-form-factor commercial off-the-shelf (COTS) dosimeters canbe worn on the body (preferably in close proximity to the ear) todirectly measure the dose in the vicinity of an individual, but theytypically lack the dynamic and frequency ranges necessary for militaryuse.

Another key challenge in translating noise surveys to individual dosesis that a dose should account for all exposure within a 24-hour period.In the absence of noise-exposure data during off-duty hours, it istypical to assume that the off-duty noise contributions are negligible,which may introduce a downward-bias on the total daily dose estimate.Furthermore, an implicit assumption of the EEH is that individualsexposed to loud noises have a recovery period following the exposurethat is at least as long as the exposure duration. This quiet recoveryperiod allows the ear to recover from TTS to normal hearing levels, andan upper limit on “effective quiet” noise levels to support TTS recoveryhas been estimated as 65-75 dBA. Moving toward 24-hour dosimetry isimportant for capturing the full daily dose of an individual and canalso allow direct measurement of the recovery conditions for anindividual.

Damage risk metrics are calculations or characteristics of the measurednoise waveforms that quantify a harmful aspect of a noise. Inconjunction with a metric, a damage risk criterion (DRC) may be definedthat enforces a limit on the metric for safe noise exposure. Themilitary and other regulating agencies set damage risk criteria thatspecify the conditions where hearing conservation measures are needed.In dosimetry, a common damage risk metric is a time-weighted average(TWA) of the A-weighted noise level:

$\begin{matrix}{{L_{{Aeq},T} = {10\; {\log_{10}\left\lbrack {\frac{1}{T}{\int_{T}{\frac{p_{A}^{2}(t)}{p_{o}^{2}}{dt}}}} \right\rbrack}}},} & (1)\end{matrix}$

where T represents the exposure duration, p_(A)(t) is the A-weightedpressure-time waveform over time T, and p_(o)=20 μPa is the referencepressure level. A common limiting criterion for this metric is 85 dBAover an eight hour period, that is, L_(Aeq,8h)≤85 dBA.

While L_(Aeq,8h) has wide acceptance as a damage risk metric forcontinuous-noise environments, many concerns have been raised that it isnot adequate for predicting hearing damage from complex or impulsivenoise. One issue is that L_(Aeq,8h) and other energy-based metricsignore much of the temporal and spectral structure of the noise, yetthere is evidence to suggest that some of these structural features areimportant in determining the severity of damage from impulsive andcomplex noise. For example, studies have shown that L_(Aeq,T)under-predicts hearing damage when continuous and impulsive noise arecombined, and other studies suggest that impulsive exposures withpredominantly low-frequency energy may be less hazardous than anequal-energy impulse dominated by higher frequencies. Additionally, thelinear relationship between energy and permanent threshold shifts (PTS)only holds for noise levels up to about 140 dB. Above this level,non-linear operations may be necessary to translate the energy metricinto auditory damage. In response to these concerns, severalcomplementary or alternative metrics have been proposed for impulsivenoise. Table 1 summarizes several damage risk metrics that have beenproposed or currently are used in hearing-conservation guidelines. Animportant caveat made in the new military standard, MIL-STD-1474E, isthat while it defines damage risk metrics and sets permissible noiselevels for the design and development of military systems, this newstandard stops short of setting limits on hearing conservationrequirements for military personnel. Therefore, the criteria in TABLE 1that reference MIL-STD-1474E should not be considered as personneldosage limits, but rather limits on the noise conditions of systems usedwithin a military environment.

TABLE 1 Metric Reference Criterion Strengths Limitations TWA NoiseMIL-STD-1474E 85 dBA for 8 h/ Simple to compute Neglects nonlinearitiesLevel NIOSH 1998 3 dB exchange Validated on human in ear (L_(Aeq,8h))and animal studies May over- or under- predict risk of impulsive noisePresumes adequate recovery conditions exist Peak Level MIL-STD-1474E 140dB Prevent mechanical Neglects temporal and (L_(peak)) NIOSH 1998 traumato ear energy characteristics Duration MIL-STE-1474D Varies withDuration with peak A-duration not (A- or B- (obsolete) L_(peak) levelsapproximates meaningful for some duration) energy impulses Very limitedtemporal characterization TWA MIL-STD-1474E 85 dBA for 8 h/ Simple tocompute A-duration not valid Impulse 3 dB exchange Builds on well- forsome impulses Noise Level established L_(Aeq,8h) Limited validation(L_(IAeq,8h)) AHAAH MIL-STD-1474E 200/500 ARU Functional model ofComplex software; ARU (U.S. Army) ear Models hearing limited to shortaudio protection and files middle-ear reflex Parameter selection effectsunclear Kurtosis Qiu et al. (2006); N/A Simple to compute Unclear howbest to Goley et al. Correlates with PTS incorporate in metrics (2011);Sun et al. in chinchilla studies Limited studies with (2016) noise fromreal-world environments Parameter selection unclear

Until recently, damage risk metrics for impulses focused primarily onlimiting the number of impulses based on peak level and duration;however, peak level and duration have failed to show a strongcorrelation with NIHL for impulsive noise. Furthermore, accuratemeasurement of the peak and duration are often complicated byreverberation and reflections which add substantial variability to thecalculated values. MIL-STD-1474E introduces two alternatives as impulsenoise damage risk metrics: (1) the L_(I Aeq,8h) metric which parallelsthe conventional L_(Aeq,8h) calculations but is explicitly defined on a100 ms interval around the impulse and includes a correction factor forlong A-duration impulses; and (2) Auditory Risk Units (ARUs) computedfrom the Auditory Hazard Assessment Algorithm for Humans (AHAAH) model.Both metrics are acknowledged to have limitations and require furtherstudy, but are considered superior to the methods of the previousstandard, MIL-STD-1474D.

One limitation of the L_(I Aeq,8h) metric is that there is littlevalidation of the A-duration correction factor (i.e., the time betweenthe onset and return to baseline pressure of an impulse) and theA-duration value itself may be uncertain. A-duration calculations arewell-suited for blast overpressure waveforms such as those from largemunitions, but challenging to measure on small weapons in the field andmay be altogether inappropriate for some impulsive noise such as thehighly reverberant impact noise from an aircraft carrier catapult.Furthermore, if A-duration is overestimated as a result of one of thecomplications just mentioned, it can lead to the undesirable effect ofunder-predicting the risk of hearing damage. For example, L_(I Aeq,8h)can be inappropriately reduced by as much as 16.5 dB for the case wherethe A-duration is overestimated at a value ≥2.5 ms.

The AHAAH model is an electro-acoustic model developed by the U.S. Armyand calculates a value in Auditory Risk Units (ARUs) that representsenergy reaching the inner ear, i.e., basilar membrane displacement. TheAHAAH model includes many software parameters, including options toactivate or deactivate the non-linear middle-ear reflex that has beenshown to limit susceptibility to hearing damage when a person isanticipating a loud noise. An additional strength of the AHAAH algorithmis that it has the ability to apply a transfer function to convertfree-field noise measurements to their expected levels at the eardrum,with the option of including suppression effects from a number ofhearing-protection devices. Having been developed as a laboratory tool,there are currently some practical considerations, however, that limitthe AHAAH model's applicability for dosimetry. The software package wasdeveloped specifically to run on short excerpts (tens of milliseconds)of an impulsive waveforms and is not well-suited for evaluating ARU overextended-duration and complex noise, which may contain a sequence ofimpulses embedded in elevated background noise. In addition to thememory and computational complications of processing extended datarecords, there is no clear process for dynamically or adaptivelycontrolling on- and offset of the middle-ear reflex where impulses mayoccur periodically but the state of an individual's middle ear reflex atany given time may not be known.

Several recent studies have sought to model auditory damage from complexnoise exposures that may be more realistic to military and industrialsettings. One concept that has shown promise is calculating kurtosis asa complement to TWA noise levels. Kurtosis is a statistical measure(fourth moment) of the data that correlates with impulsivecharacteristics in the noise. Goley et al. (2011) proposed akurtosis-corrected damage risk metric:

$\begin{matrix}{L_{{Aeq},T}^{\prime} = {L_{{Aeq},T} + {4.04\; {\log_{10}\left( \frac{\beta}{3} \right)}}}} & (2)\end{matrix}$

where β is the kurtosis of the data. L′_(Aeq,T) showed improvedcorrelation against PTS in chinchillas compared to the uncorrectedL_(Aeq,T). Recently, Sun et al. (2016) proposed an alternativekurtosis-based energy metric that adaptively elevates the effectiveenergy in impulsive noise environments and reverts to the conventionalA-weighted calculation in continuous noise environments. Further studyis needed to validate kurtosis-corrected L_(Aeq,T) over more data setsincluding complex military noise environments.

Ideal design characteristics for a noise dosimeter are shown in TABLE 2,some of which are specified in MIL-STD-1474E, in accordance with someembodiments.

TABLE 2 MIT LL MIT LL System COTS^([1]) Gen 1 Gen 2 Ideal Sample Rate 8kHz 36 kHz 128 kHz 192 kHz^([2]) Data Precision 16 bit 16 bit 24 bit 24bit^([2]) Battery Life 24 h 8 h 12-24 h Days to Weeks Measurement 70-14070-165 dBA 65-180 55-180 dBA Range dBA dBA Microphone On body On HelmetNear Ear In and/or Near Location Ear Microphone R.I.^([3]) R.I.^([3])Pressure Pressure (in-ear) Type R.I.^([3]) (near-ear)^([1])Representative Commercial off the Shelf (COTS) noise dosimeter,ANSI (1983) class 2 sound level meter ^([2])MIL-STD-1474Einstrumentation specification for impulse noise ^([3])Random-incidencemicrophone

Size, Weight, and Power (SWaP) considerations also may apply todeveloping physiological or environmental on-body sensing devices, andare relevant here as well. With a goal of a small package suitable foran on-body or in-ear system, the trade-off typically will be with therecording fidelity (sampling rate, dynamic range) and the duration overwhich data can be recorded (battery life, digital memory). While thereare many commercially available devices with a wearable form factor,they are focused on occupational noise hazards below 140 dB and employrelatively low sampling rates. In military environments, however,impulse noise often exceeds these capabilities. Portable commercialaudio recorders are one alternative to capture high fidelity noiseexposures with a calibrated microphone, but are often bulky, have manysettings and cannot process noise metrics in real-time on the device.Smartphones are another possible option, and can be accurate in certaindevice configurations and noise environments, but the built-inmicrophone typically is limited to lower sound pressure levels(non-impulse noise), low sampling frequency, and a single input channel.

FIGS. 2A-2D illustrate a system 100 suitable for capturing, digitizing,and recording high-bandwidth audio signals with relatively large dynamicrange in accordance with some embodiments. As shown in FIG. 2A, thesystem 100 includes an analog-to-digital converter (ADC) 120 coupled toa processor 130 (e.g., a microprocessor or microcontroller), which inturn is coupled to a nonvolatile (persistent, non-transitory) memory140, such as a Secure Digital (SD) nonvolatile memory card or othersuitable memory. In some cases, the system 100 may include one or morecommunications interfaces, such as an antenna 150 or a universal serialbus (USB) port 160. The system 100 may also include a power supply (notshown), such as a battery or solar cell, to power the ADC 120, processor130, and any other electronic components.

In operation, the system 100 collects analog data with a microphone 110,which captures ambient audio signals at a bandwidth of about 1 Hz or toabout 50 kHz (e.g., 5 kHz, 10 kHz, 15 kHz, 20 kHz, 25 kHz, 30 kHz, 35kHz, or 40 kHz), peak sound pressure levels of 140 dB or higher (e.g.,150 dB, 160 dB, 170 dB, or 180 dB), and at amplitude ranges of about 20dB to about 180 dB more (e.g., 80 dB, 90 dB, 100 dB, 110, 120 dB, 130dB, 140 dB, 150 dB, 160 dB, or 170 dB). The exact bandwidth andamplitude range of the microphone's output depends on the microphone 110itself; different microphones may have different amplitude or frequencyranges. The system 100 also may collect audio from more than onemicrophone at a time, e.g., as shown in FIGS. 2C and 2D and described ingreater detail below.

The ADC 120, which is coupled to the microphone's output, digitizes theanalog signal from the microphone 110 at a sample rate that is equal toor greater than the Nyquist rate for the band of interest. In otherwords, the ADC 120 samples the analog signal at a sample rate equal orgreater than twice the maximum frequency of interest in the analogsignal. An ADC may operate at sample rates of about 20 kHz to about 200kHz (e.g., 50 kHz, 60 kHz, 70 kHz, 80 kHz, 90 kHz, 100 kHz) at 16 bitsor at any other sampling rate and bit level suitable for preservinghigh-frequency audio information captured by the microphone 110. Forinstance, if the analog signal extends from DC (0 kHz) to 50 kHz, thenthe ADC 120 samples the analog signal at rate of 100 kHz or higher topreserve the information in the analog signal. (As understood by thoseof skill in the art, higher sampling rates may lead to better signalfidelity.) In other cases, the low-frequency cutoff may be higher thanDC, and the analog signal provided by the microphone may be mixed downto baseband to reduce the sampling rate or to improve fidelity given afixed sampling rate.

As the ADC 120 samples the analog signal from the microphone 110, itgenerates individual measurements, or samples, representative of theamplitude of the analog signal at respective instants in time.(Generally speaking, the higher the bit level and sampling rate of theADC, the better the quality of the digital data.) The processor 130stores these samples temporarily in one or more buffers 132 beforetransferring them to the nonvolatile memory 140. In some cases, theprocessor 130 may control the sampling and data transfer according to aninterrupt-driven process as explained in greater detail below withrespect to FIG. 3.

The nonvolatile memory 140 stores the recorded digital data for laterretrieval and processing. In some embodiments, the nonvolatile memory140 may include removable storage media, such as one or moreindustrial-grade micro SD cards (class 10/90-X), that may be wholly orpartially removed from the system 100. Using a memory card or otherremovable storage medium to store the digitized data makes the systemespecially useful in rugged environments: the memory card may be swappedin the field, e.g., at regular intervals or when it is full, for a blankmemory card enabling only limited interruptions in data collection. Andin some cases, the system 100 may store data in the buffers or inanother memory card while the full memory card is being replaced toprevent loss of data.

The system 100 may also transfer data stored in the nonvolatile memoryto other electronic devices, including servers or other externalcomputers, via the antenna 150. Depending on the application, the system100 may be wirelessly connected to a communications network, such as theinternet, cellular data communications network, or local area network,via the antenna 150 using any suitable communications standard. (Thesystem may also include an amplifier, mixer, local oscillator, or anyother component suitable for wirelessly transmitting or receivingdigital data.) In some cases, the antenna 150 may broadcast informationabout the system 100 and the captured audio data. For instance, theprocessor 130 may compress and transmit the stored audio data in one ormore brief transmission bursts at opportunistic or preprogrammedintervals. It may also transmit the digitized audio data in real-time inaddition to or instead of storing it in the memory 140. In some cases,the antenna 150 is used to save power by selectively transmitting datawhen desired and having the electronics enter into sleep mode when nottransmitting data.

The antenna 150 also may be used to receive processing instructions,firmware updates for the processor 130, or data, such as position datafrom the Global Positioning System (GPS) or any other navigation system.For example, the processor 130 may store indications of the system'slocation derived from the position data in the memory 140 with thedigitized audio information. If desired, this location information maybe used in tagging or processing the stored audio information. Theantenna 150 may also be used to reduce power consumption.

Alternatively, or in addition, the system 100 may transfer stored audioinformation to an external electronic device, such as a computing deviceconnected to a computer network, via the USB port 160. The system 100also may receive instructions, including processing instructions orupdates for the processor 130, via the USB port 160. And in someexamples, the USB port 160 may receive electrical power to power thesystem 100, to recharge the system's batteries (not shown), or both.Those of skill in the art will also readily appreciate that the systemmay include other ports (e.g., separate power ports) instead of or inaddition to the USB port 160 shown in FIGS. 2A-2D.

Extension of Dynamic Range and/or Capture of Sound Source PositionInformation

The system's amplitude range and frequency range depend in part upon thesource of the analog data—in FIG. 2A, the microphone 110. As mentionedabove, the ADC's sampling rate may be selected or controlled by theprocessor 130 to be greater than or equal to the Nyquist rate of thehighest-frequency spectral component captured by the microphone 110.Similarly, the ADC's bit depth may be selected to match or exceed themicrophone's dynamic range to preserve as much of the acquired audioinformation as possible during the digitization process.

In some cases, the system 100 may be coupled to analog signal processingcomponents to extend the amplitude range covered by the analog input tothe ADC 120. For instance, FIG. 2B shows a system 100 coupled to asingle microphone 110 via an amplifier 112, an attenuator 114, and asumming/combining node 170. Depending on the embodiment, thesumming/combining node 170 may be implemented as an analog summingcircuit, a mux digital summation using software code, or any othersuitable implementation.

In operation, the microphone 110 converts audio-frequency vibrationsinto an analog electrical signal, which is coupled in parallel to boththe amplifier 112 and the attenuator 114. The amplifier 112 amplifiesthe analog signal, e.g., with a gain of 10 dB, effectively extending thelower edge of the system's amplitude range downwards. Similarly, theattenuator 114 attenuates the analog signal, e.g., with a loss of 10 dB,to extend the upper range of the system's amplitude range. Thesumming/combining node 170 combines the resulting amplified andattenuated digital signals into a single digital signal whose amplituderange is larger than the amplitude range of the raw analog signalgenerated by the microphone 110, e.g., by an amount up to the union ofthe range(s) of each analog signal. In some cases, combining the analogsignals may increases the signal-to-noise ratio by averaging noisecommon to both analog signals.

FIG. 2C illustrates how amplification and attenuation may be used with astereo microphone system to acquire, digitize, and record stereo audiodata using the compact, portable audio recording system of FIGS. 2A and2B. In this case, the stereo microphone system includes at least twomicrophones 110-1 and 110-2 to detect sounds at different spatialpositions. For instance, microphone 110-1 may be mounted to face leftand microphone 110-2 may be mounted to face right or vice versa.

In some cases, each microphone may detect sound over a differentamplitude range. For instance, the first microphone 110-1 may produce afirst analog signal representative of sound power levels extending fromabout 115 dB to about 180 dB and the second microphone 110-2 may producea second analog signal representative of sound power levels extendingfrom about 75 dB to about 140 dB. In operation, the summing/combiningnode 170 combines the first and second analog signals to form a combinedanalog signal whose amplitude range extends from about 75 dB to about180 dB.

The system 100 also may be used to digitize, record, and process audioinformation captured by multiple microphones. In FIG. 2D, for example,the system 100 is coupled to a microphone array that includes two ormore microphones 110-1 through 110-n coupled to the ADC 120. Themicrophones may be distributed and mounted throughout a particularenvironment using any suitable mounting scheme. For instance, they maybe distributed about the circumference of a hard-hat or helmet and wiredto the ADC 120 (possibly via one or more other active or passiveelectronic components). The microphones also may be distributedthroughout a rugged environment (e.g., on different hard-hats orhelmets, on different vehicles, etc.) and wirelessly coupled to the ADC120 (e.g., via wireless links), with accelerometers, gyrometers, GPSreceivers, or other navigational aids used to track their relativeand/or absolute positions and orientations for processing and analysis.

Each microphone in the microphone array provides a separate audiosignal, or track, that represents audio-frequency waves sensed by at themicrophone's locations. These audio tracks may be combined (e.g.,summed) to form a single analog signal that is sampled by the ADC 120 ata sampling rate equal to or greater than the Nyquist frequency of thehighest-frequency spectral component. Alternatively, the ADC 120 maysample each audio track in a round-robin/interleaved fashion, e.g.,first by sampling the analog signal from microphone 110-1, then bysampling the analog signal from 110-2, and so on. The system 100 alsomay include multiple ADCs (not shown), each of which is dedicated to oneor more respective microphones in the microphone array.

In certain embodiments, the processor 130 may use multi-track audioinformation acquired by the microphone array to estimate the relativelocation of the source of a particular sound or to identify differenttypes of sounds. For instance, the processor 130 may use stereo ormulti-track audio information to distinguish a blast or gunshot from asignal caused by dropping a microphone on the ground. Post-processingalso may be used to identify sounds and their locations. If desired, theaudio track from a microphone may be amplified, attenuated, and/ordelayed using the appropriate analog components placed in series withthe microphone in the microphone array to extend the dynamic range,increase sensitivity, etc., as described further herein.

Interrupt-Driven Processing Modes

As mentioned above, the system 100 shown in FIGS. 2A-2D may operate ininterrupt-driven processing modes to improve sampling consistency,reduce power consumption, and reduce interference between writing datato memory and sampling the analog signal. In one mode, the system uses atimer, such as a software timer implemented in the processor, tointerrupt data transfer from the buffer(s) to the nonvolatile memoryduring each sampling operation by the ADC. In another mode, the ADCinterrupts the data transfer each time it generates a new sample.Depending on the implementation, the system may be hard-wired to operatein either the timer-driven interrupt mode or the ADC-driven interruptmode or configured to switch between the two.

FIG. 3 illustrates a process 300 with sub-processes and functions commonto the timer-driven interrupt mode and the ADC-driven interrupt mode. Inboth interrupt modes, the system 100 executes a setup function 310, thena main loop 320 for logging data and performing other functions. Thesetup function 310 is executed once the device is powered on and mayinclude assigning the input and output pins on the microprocessor,initializing the system's identification (ID) number (e.g., for usingmultiple devices), setting the analog-to-digital conversion resolution(e.g., to 16 bits), and creating a file in the nonvolatile memory toreceive the digitized data from the buffer(s). In some examples, the ADCresolution may switched “on the fly” among different settings, e.g., toany bit level from about 8 bits to about 24 bits. For instance,switching to a higher resolution increases data fidelity, and switchingto a lower resolution may increase battery life, decrease processingtime, and make more conservative use of storage capacity.

Once setup 310 is complete, the system enters the main loop 320 andexecutes this main loop 320 repeatedly until the system is powered down(e.g., turned off by the user) as part of a shutdown function 330. Insome implementations, the main loop 320 includes a large case/switchstatement in which the system switches among different states inresponse to various user actions, like plugging the device into acomputer or screwing in the magnetic screw. These states include astandby state 322, a charging state 324, and a data logging state 326.The processor may call different functions in each state, depending onuser input, remaining battery charge, environmental conditions, etc.

In the standby state 322, the system waits to be connected to a computerto download data and/or charge, or for a user input 327 that causesrecording to begin, such as screwing in a magnetic screw as describedfurther below. While in the standby state 322, the processor may callfunctions that check for a USB connection or other connection to anexternal computer, for the battery voltage level (e.g., with respect toa “turn-off” threshold), and for user input 327 that (e.g., as indicatedby a changed in voltage from a Hall effect sensor that senses theposition of a magnetic screw).

The system enters the charging state 324 when it is connected to acomputer or other electronic device, e.g., by plugging a cable into itsUSB port 325. Once the system is connected to the computer, it switchesinto a “reader mode” in which the contents of the nonvolatile memory(e.g., an SD card) may be accessed from the computer. Once in this mode,the system calls the appropriate functions to monitor the charging ofthe battery. The system stops charging if the battery temperatureincreases above a threshold temperature (e.g., set to prevent batteryfailure), the battery voltage reaches a voltage threshold, or thebattery has been charging for more than a predetermined period (e.g., afew hours). These failsafe measures may prevent the battery fromcharging incorrectly or exploding.

In the charging state 324, the processor may call functions that returnthe battery temperature (e.g., in degrees Celsius or Fahrenheit),calculate the battery voltage, and return the battery voltage. Otherfunctions, typically used in debugging, may print the battery's voltage,temperature, or both. The processor may also call functions thatinitialize internal variables to monitor battery charging and thatmonitor the battery's voltage and temperature during charging. And theprocessor may check for the USB connection.

In addition, the processor may turn off and on the power to thenonvolatile memory (SD card) in the charging state 324. This enables thesystem to switch from a mode in which the processor may log data to thenonvolatile memory to another mode in which the nonvolatile memory maybe viewed from the computer as a data drive. This function is calledonce the device has been connected to the computer via a USB port orother connection. Another function enables the nonvolatile memory to beviewed from the computer as a data drive. The processor may execute yetanother function in which the nonvolatile memory is “ejected” from thecomputer, allowing the processor to access the nonvolatile memory forwriting purposes.

Actuating the device, e.g., by throwing a switch or screwing a magneticscrew (step 327), causes the system to enter the data logging state 326.In response, the processor reads the system's configuration file andselects the appropriate data acquisition mode, e.g., full-resolutionaudio acquisition mode or low-resolution dosimeter mode. Once theprocessor has selected the data acquisition mode, it executes a datalogging function in which it writes the data from buffers that are fullto the nonvolatile memory.

Generally, the period taken the write data from a given buffer to thenonvolatile memory is less than the sampling period (the reciprocal ofthe sampling rate). In some cases, the buffer size is chosen to matchthe page size of the nonvolatile memory to reduce the amount of timerequired to write the data from the buffer to the nonvolatile memory.For example, the buffer size may be about 512 bytes, which matches thepage size of certain SD cards. Because the buffer size matches the SDcard's page size, the processor may write the data to the SD card incontiguous chunks (e.g., one page at a time), which tends to be fasterthan splitting the data into fragments and writing the fragments tonon-contiguous portions of the SD card.

The processor continues to loop, checking to see if any buffers arefull, until the magnetic screw is unplugged, the battery dies, oranother signal stops execution of the data logging function 326.Unscrewing the magnetic screw or throwing the switch again causes thesystem to enter the standby state 322, and connecting the system to acomputer may cause the system to enter the charging state 324. Uponexiting the data logging state 326, the processor closes any files openin the nonvolatile memory and returns to the main loop 320 to changestates.

If the processor senses that the battery voltage is about to fall belowa certain threshold voltage (e.g., 3.3 V) or the battery dies, thedevice stops recording and shuts down (shutdown state 330). In somecases, this threshold voltage may equal to or greater than the voltageneeded to write to the nonvolatile memory. The system may remain in theshutdown state 330 until power is disconnected and reconnected.

In data sampling and logging with timer- and ADC-based interrupts, theprocessor executes a data logging loop in which it checks to see whetheror not it has filled the buffers with data generated by the ADC. If aparticular buffer is full, the processor writes the data from thatbuffer to the nonvolatile memory, leaving some buffer space free to holdsamples collected by the ADC during the data transfer from the buffer tothe nonvolatile memory. If no buffer is full, then processor waitsbefore checking the buffer status again until the user switches thesystem to standby mode or charging mode, the battery dies, or theprocessor receives an interrupt signal from a timer or the ADC.

To interrupt the data logging process using a timer interrupt, theprocessor maintains a timer that counts clock ticks from a clock runningat a clock rate (e.g., 48 MHz) faster than the ADC's sampling rate(e.g., 35 kHz). The clock drives the ADC by effectively setting thesampling rate to a fraction of the clock rate: when the timer reaches apredetermined threshold, the processor initiates an ADC samplecollection. It also interrupts the data logging process and resets thetimer, e.g., immediately after the timer overflows. Once samplecollection is complete, the processor resumes the data logging processby storing the sample in a buffer and returns to the interrupted bufferchecking or data writing step. When the timer overflows again, theprocessor initiates the next ADC sample collection, interrupts datalogging, resets the timer, etc. The frequency of the timer interrupt(and hence the sampling rate) may be varied by changing the clock rateor timer threshold. For instance, the ADC sampling rate may berelatively low (e.g., about 1 Hz) for collecting low-resolution noisedosimetry data and relatively high (e.g., about 100 kHz) for collectinghigh-resolution audio data.

The ADC-driven interrupt approach uses sample collection rather than anexternal clock to trigger interruption of the data logging process. Inthis approach, the clock initiates a first ADC callback to startsampling. After the first callback, the ADC asynchronously initiates aninterrupt after collection of each sample. In other words, the ADCsamples the analog signal at the sample rate and interrupts the datatransfer from the buffer(s) to the nonvolatile memory when it generatesa sample. After the processor writes the sample to a buffer, the datatransfer process resumes as in the timer-driven approach. Hooks in thecode handle anomalies associated with the nonvolatile memory, e.g., suchas when shifting from a USB reader to internal writing. Another hookdetects whether or not the user has switched the system out of datalogging mode, e.g., by checking for a magnet close enough to trigger theHall effect sensor (on/off sensor) as described further below. Dependingon the implementation, the ADC-driven interrupt approach may supporthigher sampling rates than the timer-driven interrupt approach. Inaddition, an external ADC suitable for supporting the ADC-driveninterrupt approach may consume less power than a microprocessor used tomaintain a software timer for the timer-driven interrupt approach.

Impulsive Sound Measurements

FIG. 4A shows a spectrogram (top) and a plot of sound amplitude versustime (bottom) of gunshots from two M27 Infantry Automatic Rifles firingat 14 rounds per second captured by an audio recording system. FIG. 4Bshows close-ups of the spectrogram (top) and waveform (bottom) shown inFIG. 4A; these close-ups correspond to a single gunshot. To capture thegunshots, the system acquired sound with a pair of balanced microphoneslike those shown in FIG. 1C over a bandwidth of about 17 kHz anddigitized the resulting analog signal at a sampling rate of about 34kHz. The “low-channel” microphone acquired sound pressure levels ofabout 80-140 dB, and the high-channel microphone acquired sound pressurelevels spanning about 110-170 dB.

The spectrogram and the plot in FIG. 4A both show large peaks associatedwith gunshots from the first M27 interleaved with intermittent smallerpeaks form a second M27 that is farther from the microphone. The maximumrecorded sound pressure level is about 148 dB. The spectrogram shown inFIG. 4B shows the power spectral density associated with a singlegunshot. And the plot in FIG. 4B shows that rise time of the resultingdigital signal is less than 60 μs.

Speech Content Removal

If desired, the processor may irreversibly blur or scramble at least aportion of the acquired audio data without removing spectral or temporalinformation associated with the impulsive sounds. In other words, theprocessor may permanently remove information in one or more sub-bands ofthe acquired audio data without substantially affecting the ability tomeasure the temporal, spectral, and amplitude characteristics ofimpulsive sounds. For instance, the processor may remove speech contentfrom the digital data stored in the nonvolatile memory, e.g., tominimize operational security risks on battlefields and other hostileenvironments, to preserve confidential information, or to meetnon-disclosure obligations. This processing effectively “washes” out thephonetic and thus syllabic structure of speech, while retaining much ofthe temporal and spectral information used for noise induced hearingloss (NIHL) modeling, including impulsive-like sounds (e.g., gun shotsand explosions) and steady and repetitive background sounds (e.g.,vehicle and machine noises). Speech content and information in othersub-bands also may be removed during post-processing after the data istransferred from the nonvolatile memory to another computer or computingdevice.

FIG. 5 illustrates a processing strategy 500 suitable for removingspeech content or other information from a digitized audio sample whilepreserving background noise levels and impulsive sounds. The system'sprocessor (not shown) splits an original sound clip 50, which may bestored as one or more digital samples in either the system's buffer orthe nonvolatile memory, into two outputs: a first output 510 thatrepresents a background noise estimate, and a second output 520 thatrepresents recorded impulse sounds. If desired, these outputs may berecorded directly on the nonvolatile memory to prevent the system fromrecording speech content (or other information) in the nonvolatilememory.

To generate the first output 510, the processor calculates the energy in32 logarithmically spaced frequency bands. This energy is sampled over atime interval of 200 ms, resulting in a downsampling of both time andfrequency. This process of calculating the spectral energy distributionand temporal downsampling is roughly analogous to the blurring out of aperson or face on television. In addition, the process of generating thefirst output 510 is lossy such that the first output 510 cannot beinverted to recover speech content.

The first output 510 may be generated using other frequency spacings,including logarithmic spacings, linear spacings, octave spacings, andfractional octave spacings, and other sampling intervals. Depending onhow many frequency bands used and the time-averaging window, however, itmay be possible to reconstruct a comprehensible estimate of the originalspeech waveform. But selecting downsampling parameters for the“blurring” process using perceptual and objective measures results in afirst output 510 with enough spectral and intensity information toinform a NIHL model, but not enough to understand speech in areconstructed signal.

The processor generates the second output 520 by filtering out sampleswhose amplitude falls below a particular amplitude threshold and/orwhose duration exceeds a particular duration threshold. Depending on theapplication, the amplitude and duration thresholds may be chosen tocapture impulsive noise events. To remove speech content but notgunshots, for example, the processor filters out samples correspondingto sound pressure levels below about 125 dB, which is louder than theloudest sound produced by a single human voice, and durations longerthan about 40 ms to about 70 ms. Because a single human voice cannotproduce sound this loud, the second output 520 does not includeconversational human speech. Rather, it includes very loud impulsivesounds, such as weapons fire, blasts, etc. Like the first output 510,the second output 520 is generated via a lossy process and does notinclude any recoverable speech content.

Even if a single sample in the second output 520 includes both speechand very loud impulsive sounds, it may be difficult to separate andrecover the speech for at least two reasons. First, capture of thewaveform would be only about 40 ms to about 70 ms long, which is halfthe duration of a typical speech sound (less than the length of a singleword). Second, because an impulsive sound such as gunfire is so muchlouder than speech (even shouting), the signal-to-noise ratio of therecording devices would likely prohibit hearing or separating the muchsofter speech from the gunfire.

Those of skill in the art will readily appreciate that processingstrategy 500 illustrated in FIG. 5 may be used to remove otherinformation instead of (or in addition to) speech content. For example,the second output could be generated by removing very loud impulsivesounds (e.g., sounds with amplitudes over 125 dB and durations of lessthan about 70 ms). Similarly, the frequency and time bins selected togenerate the first output 510 may be selected to blur low-frequencymechanical sounds without unduly affecting speech content.

FIGS. 6A and 6B show spectrograms of single spoken sentence before andafter processing, respectively. Both spectrograms show the temporalevolution of spectral energy of the processed signal, where redindicates higher energy and blue less energy. And the pixelation in FIG.6B shows that processing permanently removes speech content informationfrom the signal.

The performance of the processing strategy 500 shown in FIG. 5 wastested with a perceptual pilot test to identify words and sentencesafter the speech removal process. The test material included recordingsof five phonetically and syllabically balanced spoken sentences with aquiet background, and recordings of five similar spoken sentences withelectronically added weapons fire. These recordings were processed usingthe processing strategy illustrated in FIG. 5 before being interpretedby four test participants, three of whom were experts in speech andacoustic analysis. The test participants were permitted to listen toeach sentence as many times as desired and instructed to write down anywords they perceived.

Although the test participants were able to guess at the words in eachprocessed recording, they indicated that they had very little to noconfidence in their answers. Overall, the participants identified about1% of the key words in the quiet environment and about 0% of the wordswith weapons fire. (The participants' ability to identity about 1% ofthe words in the quiet recordings does not mean that the speech wascomprehensible because the words in the selected sentences are commonlyused and it is possible to guess correctly.) These results demonstratethat even someone with extensive knowledge of sound and speech would beunlikely to interpret speech in a file processed using the speechremoval algorithm.

FIGS. 7A and 7B illustrate an analysis of processed speech thatquantifies the ability to segregate vowels automatically (e.g., using acomputer). FIG. 7A shows the temporal waveform (upper plot) andspectrogram (lower plot) associated with an unprocessed speech signal.FIG. 7B shows the temporal waveform (upper plot) and spectrogram (lowerplot) of the same speech signal after the processing described above.Both spectrograms include red lines indicating vocal tract resonances(referred to as formants in the speech community) F1 and F2 and bluelines indicating pitch tracks estimated using a phonetic analysissoftware tool (e.g., Praat computer software, available fromwww.praat.org).

FIGS. 7A and 7B show that if vocal tract resonant structure is notpresent in the speech signal, then recognition by computer (or human)may not be possible. This is because the lack of vocal tract resonantstructure implies a removal of phonetic and thus syllabic structure ofspeech. Comparing FIGS. 7A and 7B shows that the original speech signal(FIG. 7A) contains dynamic formants F1 and F2 that vary during thevoiced (vowel) segments of the speech (indicated by darker horizontalbands in the spectrogram). The time variations are required in phoneticand syllabic identity. The processed speech in FIG. 7B, on the otherhand, shows very noisy and inaccurate estimates of the formants, whichbarely vary for the different vowels across the sentence.

Using a database of 540 English vowels over 45 speakers gives moreinsight into how processing removes formant information. In theunprocessed speech, the different groupings are somewhat separated bytheir F1 and F2 formants, and are therefore fairly distinguishable fromone another, which is not necessarily the case in the processed speech.This helps to explain why the test participants had trouble identifyingwords in the perceptual test. Using a k-means clustering algorithm toclassify each vowel in the database resulted in a 56% classificationrate in the unprocessed speech, while only 19% after processing. Thisdecrease in clustering accuracy is a direct result of the informationlost permanently by the processing scheme, and gives a relative metricof how much formant information is lost.

Distinction of Impulse Noise Via an Inertial Measurement Device

Inadvertent physical contacts, shocks, or tremors between a microphoneand matter (e.g., wind or the brushing of a tree branch against ahelmet-mounted microphone) may be mistaken as impulse noise, forexample, on a battle field. A “false impulse signal” can pollute and mayinevitably interfere with a noise exposure calculation. In someembodiments, an inertial measurement device (IMU), such as a tri-axialaccelerometer, may be co-located with a microphone to account for falseimpulse signals from physical contacts with the microphone. For example,a physical shock to the helmet or body can be treated as an impulsethereby interfering with a noise exposure calculation. Using a tri-axialaccelerometer as a “truth sensor,” some embodiments may require no or alow acceleration response from an accelerometer to determine that arecorded impulse signal represents actual impulse noise. For example,embodiments with this augmentation may mitigate false impulse signals inthe rain from rain drops continuously hitting the microphone.

FIG. 8 is a block diagram illustrating the accelerometer false impulserejection model in accordance with some embodiments. Threetime-synchronized data streams may be retained from a single dosimeter,including near-ear audio data 850, near-ear accelerometry data 852, andon-body accelerometry data 854. The near-ear audio data 850 may includetwo gain channels of audio from at least one and/or each near-earmicrophone in the dosimeter. The near-ear accelerometry data 852 mayinclude tri-axial accelerometry data from at least one and/or eachnear-ear accelerometer in the dosimeter. The on-body accelerometry data854 may include 9-channel accelerometry data from at least one and/oreach IMU located in body-worn electronics housing. Audio datapreprocessing 856 may include removing DC offset and combining gainchannels. Accelerometry data preprocessing 858, 860 may include removingDC offset and resampling to match the audio sampling rate. Additionally,accelerometry data may be high-pass filtered to remove low-frequencycomponents associated with typical human motion and filtered to combinethe tri-axial signals. Shock-artifact detection 862 may includeanalyzing time frames of the preprocessed accelerometry data forpresence of possible shock artifacts. Candidate shock artifact framesmay be identified using a threshold on the filtered signal or athreshold on cross-correlated data streams (e.g., left ear accelerometervs. right ear accelerometer or left ear accelerometer vs. left earaudio). Frequency-selective background noise removal 864 may be appliedto the audio channels to isolate shock-like noise from continuousbackground noise. The background-removed audio signal may be fed into anadaptive filter 866. For candidate frames where a shock artifact isdetected, adaptive filter weights may be trained to transform the on-earaccelerometry data to match the background-removed audio signal whichpotentially contains shock artifacts. The adaptive filter weights maycombine current and delayed time samples to predict audio responseinduced by a physical shock. The output of the adaptive filter 866,which estimates the audio shock artifact in a candidate frame, issubtracted from the audio channels at node 868 to produce shock-artifactfree audio 870.

Calibration of Noise Levels Via an Altimeter and/or Barometer

Noises, both impulsive and continuous, are pressure waves that can beinfluenced by environmental conditions. Knowledge of the air pressurelevel, which can fluctuate over hours to days, in an environment isimportant in order to accurately calculate the sound pressure level. Byusing or including an on-board altimeter device (for measuringbarometric pressure), along with a temperature and humidity sensor, someembodiments can determine the air pressure in the surroundingenvironment. The air pressure, temperature, and humidity measurementsmay be fed, in real-time, to the sound pressure level (SPL) calculationto better gauge noise levels. By taking into calculation the base-levelpressure, temperature, and humidity changes due to altitude or otherwisediffering noise environments, the SPL calculation then may be calibratedproperly regardless of the environment or fluctuations within aparticular environment over time.

Dynamic Range Extension Via Channel Combination

A dosimeter system may be designed to have a high dynamic range. In someembodiments, an analog signal (e.g., from a single microphone) may bepassed through a first amplifier with gain and a second amplifier withattenuation before digitization. In other embodiments, two sensors(e.g., two microphones) may be used, the first sensor with highersensitivity and the second sensor with lower sensitivity to span abroader range. For example, the second sensor may be apiezoelectric-type pressure sensor that captures high pressure levels upbeyond 50 PSI (for capturing, e.g., blast levels). The two analogsignals are then digitized separately.

In some embodiments, optimum gain matching factor ϵ_(max) minimizes theleast squares difference between two channel arrays:

$\begin{matrix}{{\min\limits_{\in}\left\{ {\sum\limits_{n}\left\lbrack {{\left( {{g\lbrack n\rbrack} - {\langle g\rangle}} \right) -} \in \left( {{f\lbrack n\rbrack} - {\langle f\rangle}} \right)} \right\rbrack^{2}} \right\}},} & (3)\end{matrix}$

where g[n] and f[n] are the two arrays following the ADC conversionstep, and <.> denotes the mean over the array elements. This representsthe most general form of the gain-matching factor, and may be applied toany continuous subset of the full arrays. The size of the subsets may beselected based on the desired frequency response for the gain-matchingfactor.

Without loss of generality, the following variables may be defined as:

g′[n]≡g[n]−<g>  (4)

f′[n]≡[n]−<f>  (5)

R≡Σ _(n) [g′[n]−−f′[n]] ²  (6)

The optimum gain matching factor ϵ_(max) is found by minimizing R, theleast squares difference between the two arrays, in the followingmanner:

$\begin{matrix}\begin{matrix}{{\frac{\partial R}{\partial \in} = {\sum\limits_{n}{2\left( {{{g^{\prime}\lbrack n\rbrack} -} \in {f^{\prime}\lbrack n\rbrack}} \right)\left( {- {f^{\prime}\lbrack n\rbrack}} \right)}}}\;} \\{= {{{{- 2}{\sum\limits_{n}{{g^{\prime}\lbrack n\rbrack}{f^{\prime}\lbrack n\rbrack}}}} + {2\sum\limits_{n}}} \in {f^{\prime}\lbrack n\rbrack}^{2}}}\end{matrix} & (7)\end{matrix}$

Thus, a single global extremum is found when

$\left. \frac{\partial R}{\partial \in} \right|_{\in {= \in_{\max}}} = {0\text{:}}$

$\begin{matrix}{\in_{\max}{= \frac{\sum\limits_{n}{{g^{\prime}\lbrack n\rbrack}{f^{\prime}\lbrack n\rbrack}}}{\sum\limits_{n}{f^{\prime}\lbrack n\rbrack}^{2}}}} & (8)\end{matrix}$

Due to the positivity of R and unchanging convexity of R with respect to∈, it is guaranteed that ϵ_(max) minimizes R.

The two digital signals or channels are then combined through a channelselector or weighted sum to reduce signal discontinuities acrossdifferent types of sensors (e.g., condenser microphones vs.piezoelectric sensors). Channel selection may be performed by amultiplexor between g[n] and Âf[n] based on the signal amplitude (e.g.,switch at 90% of the lower gain channel), or through a weighted sum ofthe two channels dependent on the signal-to-noise ratio.

FIG. 9A is a block diagram illustrating how this algorithm for channelcombination may be used with a stereo microphone system to extenddynamic range in accordance with some embodiments. In this case, thestereo microphone system includes at least two microphones 110-1 and110-2 to convert audio-frequency vibrations into an analog electricalsignals at different spatial positions. In some cases, the microphonesmay detect sound over different amplitude ranges. Microphone 110-1 iscoupled in parallel to amplifier 112 to amplify the analog signal andeffectively extend the lower edge of the system's amplitude rangedownwards. Microphone 110-2 is coupled in parallel to attenuator 114 toattenuate the analog signal and effectively extend the upper range ofthe system's amplitude range. The two signals are digitized separatelyby ADC 120, subjected to DC offset removal 880, subjected to gainmatching 882, and finally combined through channel selection 884.

This algorithm for channel combination has been prototyped and tested.FIG. 9B is a series of plots illustrating an implementation of the fullalgorithm for an impulsive noise source, with an attenuation and gainchannel on the same pressure sensor, in accordance with someembodiments. Plot 892 shows that the attenuation channel is selectedduring the high part of the impulsive noise source (158-162 ms). Duringa lower amplitude portion of the signal shown in plot 894, the gainchannel is selected for its higher SNR. Note that the signals are wellmatched, even though they are put through two different amplificationcircuits. The gain matching and simple multiplexor used in this exampledemonstrates the effectiveness of this algorithm for channel stacking.

Additional On-Board Processing Improvements

In some embodiments, dosimeter signal processing is improved using analgorithm for removing false impulse signals by comparing data from anIMU/accelerometer against data from a microphone. The extraction andcomparison of data originating from similar instances of impulses fromthe two components may help identify and remove false impulse signals ina noise recording. Analysis of signal frequencies and amplitudes, orgeneral characteristics of the impulse (e.g., rise time, decay, ringing,etc.) may be used to differentiate a physical shock from a sound impulseand/or continuous noise.

In some embodiments, noise exposure estimates are improved using analgorithm for obtaining real-time barometric pressure by measuringbarometric pressure, temperature, and humidity with at least one sensorand comparing the measurements with the environmental air pressurelevels, as described above.

In some embodiments, noise exposure estimates are improved using analgorithm for real time SPL and event data logging via the use of GPS oranother navigation system. By recording both impulses (and/or sustainedcontinuous noise) and tagging the noise events with geo-temporal stamps,some embodiments may help create an event log (“breadcrumbs”)documenting where multiple noise events have taken place over a periodof time.

In some embodiments, noise exposure estimates are improved using analgorithm to calculate real time noise exposure using information fromdisparate sources, such as noise recordings, barometric pressurereadings, and geo-temporal stamps.

Geo-Temporal Stamping of Impulse Noise

In some embodiments, a location of an impulse noise reading may betagged using, for example, a satellite navigation device, such as a GPSreceiver. The GPS receiver may be on board the dosimeter. In addition totagging a location to an impulse noise event, a GPS receiver may be usedto help characterize the impulse noise event by recording a time-stampfor the event. This may help to further characterize a noise exposureevent by providing geo-temporal stamps in the truth data. In someembodiments, geo-temporal stamps may be collected from multipledisparate units (i.e., users) to localize a signal and/or reconstruct asignal profile in time and space by tracking of the sequence of multipleand/or related noise events.

FIG. 10A is a display illustrating a map with symbols indicatinglocations corresponding to noise exposure events and a user positionrelative to those locations in accordance with some embodiments.

Networked Communications of Multiple Users in the Field

In some embodiments, network capabilities are provided. Informationgathered and acquired from multiple users in a network may besynchronized and used to determine if one or more members or all themembers in the network are overexposed. A networked capability in thiscapacity within the framework of the system may help to locate animpulsive noise event and can be of great benefit. If the relativeposition of each member is known when a noise event occurs, thegeo-temporal stamps (with associated SPL level) can be shared throughnetworking. If multiple individuals are exposed, any additionalinformation may be used to locate or better locate the direction andpossible distance of the noise source. The information also may be sentto, for example, a supervisor or squad/team leader to report or display(on, e.g., a cell phone) the status of individuals (e.g., at least oneor each member of the squad/team).

FIG. 10B is a display illustrating a map with symbols indicating alocation corresponding to a noise exposure event based on positions ofmultiple users relative to the location in accordance with someembodiments.

Behavioral Influence Via Feedback

In some embodiments, if a user is exposed to a predetermined number ofhigh continuous or impulsive noise events or high continuous orimpulsive noise for a predetermined period of time, feedback may beprovided to the individual, for example, “Your daily threshold for noiseexposure has been exceeded.” The cumulative and compounding noiseexposure can severely damage hearing, and hence this information may beused to influence the behavior of an individual (e.g., promote hearingprotection and/or alter behavioral responses toward high levels ofimpulsive noise). The information also may be sent to, for example, asupervisor or squad/team leader to report or display (on, e.g., a cellphone) the status of individuals (e.g., at least one or each member ofthe squad/team).

Implantable and Array Embodiments

With the advances in microelectronics, such as systems on a chip (SOC),which are low size weight and power (SWaP) high capability devices, adosimeter may be miniaturized for greater portability and even forimplanting in a user. For example, micro-dosimeters deployingimplantable surface or sub-dermal microphones may record noise events.If a miniaturized SOC has a built-in communication component, therecorded noise events may be processed and sent off via a radiocommunicatively coupled to the dosimeter. In some embodiments,micro-dosimeters may be implanted inside or near a user's ear foraccurate ear canal-like noise measurements. In some embodiments,multiple micro-dosimeters may be implanted to form a ring or array ofsensors around the head, such that the array of sensors records noisebut also collects directionality of noise events. Augmenting thiscapability with other scalar information, such as geo-temporal stamps,the system may further enhance the recreation of the noise events.

FIG. 11 is a diagram illustrating a micro-dosimeter to be attached to orimplanted in a subject (e.g., near or inside each of a subject's ears)in accordance with some embodiments.

Self-Calibration for Individual Users

In some embodiments, a system includes a calibration tone that is playedperiodically into the microphones for routine calibration of frequencyand amplitude responses of the microphones. As such, the hardwareresponse to a certain noise (dB) level may be maintained and calibratedto ensure that the microphones are working properly at all times. Insome embodiments, the system also includes a pressure chamber (with apredefined pressure level) around the altimeter to periodicallydetermine and/or monitor the altimeter response to a known pressurelevel. The pressure chamber then may be open to the environment duringnormal operation and, while not in active use, it may be closed and/orpressurized for self-calibration.

Examples

Noise exposure and the subsequent hearing loss are well documentedaspects of military life. Numerous studies have indicated high rates ofNIHL in active-duty service men and women, and recent statistics fromthe U.S. Department of Veterans Affairs indicate a population ofveterans with hearing loss that is growing at an increasing rate. In aneffort to minimize hearing loss, the U.S. Department of Defense (DoD)updated its Hearing Conservation Program in 2010, and also has recentlyrevised the DoD Design Criteria Standard Noise Limits (MIL-STD-1474E),which defines allowable noise levels in the design of all militaryacquisitions including weapons and vehicles. Even with such mandates, itremains a challenge to accurately quantify the noise exposureexperienced by an individual over the course of a mission or trainingexercise, or even in a standard work day. Noise dosimeters are intendedfor exactly this purpose, but variations in device placement (e.g.,free-field, on-body, in/near-ear), hardware (e.g., microphone,analog-to-digital converter), measurement time (e.g., work day,24-hour), and dose metric calculations (e.g., time-weighted energy, peaklevels, Auditory Risk Units), as well as noise types (e.g., continuous,intermittent, impulsive) can cause exposure measurements to beincomplete, inaccurate, or inappropriate for a given situation.

Some embodiments are directed to predictive modeling of, recording,and/or processing sound pressure in an environment subject to bothcontinuous noise and impulse noise, both of which may contribute toNIHL. According to some embodiments, a noise dosimeter capable ofacquiring exposure data across tactical environments is disclosed. Tohelp fill the gap in dosimetry technology appropriate for the military,Massachusetts Institute of Technology Lincoln Laboratory (MIT LL) isdeveloping a noise dosimeter with the goals of capturing noise exposurefor individuals through on-body sensors and providing acousticcharacterization of both continuous and impulsive sounds.

Two generations of prototypes have been constructed and tested. Thefirst-generation prototype device was fielded in 2013 with dismountedMarines in Afghanistan by the Marine Expeditionary Rifle Squad (MERS) aspart of a joint protocol with the U.S. Army Research Institute ofEnvironmental Medicine (USARIEM). The second generation prototype is alaboratory-grade, portable dosimeter that is funded jointly by MERS andthe U.S. Army Natick Soldier Research, Development, and EngineeringCenter (NSRDEC). In accordance with some embodiments, thesecond-generation device will meet nearly all the instrumentationstandards for impulse noise outlined in MIL-STD-1474E and provideadditional functionality and sensors, such as accelerometers to helpfilter out false noise events from objects hitting the microphones.Further details about the prototypes are provided below.

Utilizing embodiments for on-body measurements and collectingcoordinated audiometric tests on individuals during military operationsmay generate important data sets for evaluating existing noise metricsand validating new ones. Opportunistic data collections of this typeduring military operations may reduce reliance on the unique Albuquerqueblast overpressure walk up study (Johnson, 1993) and may help to informindividual susceptibility for NIHL by including other physiological andgenetic factors.

FIG. 12 is an image illustrating the first-generation prototype inaccordance with some embodiments. In FIG. 12, the compact, portablesystem 100 and stereo microphone system 110 of FIG. 2C are mounted onthe back of a helmet 200 for collecting audio data in a ruggedenvironment (e.g., a battlefield). Stereo microphones 110-1 and 110-2(not shown) are mounted on the left and right sides of the helmet, closeto the ears of the person wearing the helmet 200. Because themicrophones are mounted near the person's ears instead of elsewhere,they capture audio data that more accurately represents what the personactually hears while wearing the helmet 200.

System 100 may be used to record ambient sounds with a peak amplitude ofabout 180 dB at a bandwidth of about 50 kHz. In one case, the system'samplitude range extends from about 81 dB to about 173 dB, for a totaldynamic range of about 92 dB, with the stereo channels from eachmicrophone spanning about 58 dB each. The recorded digital datapreserves the spectral characteristics of the ambient sounds andcaptures the rise time and spacing of impulsive sounds (e.g., gunshotsand explosions) within earshot of the person wearing the helmet 200.

Depending on the battery life, memory size, and device temperature, thesystem 100 may record for up to 24 hours without imposing anyunacceptable risks (e.g., of battery explosion) on the person wearingthe helmet 200 or others near the helmet 200. For instance, the batterylife and memory size may be long enough and large enough, respectively,to support eight hours or more of continuous 16-bit recording at abandwidth of about 32 kHz. If desired, system 100 may be reprogrammed orswitched among operating modes to extend the collection period. In onemode, system 100 may act as a noise dosimeter that records only the peakaudio levels integrated across some or all of the audio band; in anothermode, system 100 may record high-resolution audio data.

As shown in FIG. 12, system 100 may be mounted in a housing 190 with asmall, robust form factor. In the example shown in FIG. 12, the housing190 has exterior dimensions of about 80 mm×55 mm×10 mm and is made ofinjection-molded plastic, resin, or any other suitable material. Thehousing 190 includes outer halves 192 and 194 that fit together to forman enclosed cavity, which holds a circuit board 152 with the electroniccomponents (ADC 120, processor 130, etc.) shown in FIGS. 1A-1D and abattery 154, such as an NiMH or NiCad rechargeable battery, that powersthe circuit board 152. Audio jacks 198 fit into apertures in the housing190 and connect the components on the circuit board 152 to themicrophones. When fully assembled, system 100 (including the housing190) weighs about 40 g or less, including the microphones.

A magnetic screw 196 or other actuator, such as a switch, may be used toturn the system 100 on or off. For example, tightening the magneticscrew 196 moves the magnet 196 in closer to a Hall effect sensor (notshown), which produces an output whose voltage changes in response tothe increase in magnetic field strength. The processor, which is coupledto the Hall effect sensor, detects this change in voltage and starts therecording process. Loosening the magnetic screw 196 reduces the magneticfield sensed the by the Hall effect sensor, which produces anothervoltage change that causes the processor to stop recording audio data.

A wearable, high sampling rate, broad spectrum noise dosimeter attachedto a helmet may include at least two microphones co-located, forexample, with an accelerometer to detect false impulse signals.According to some embodiments, the system is capable of self-calibrationand provides real-time processing of data including voice removal, peakamplitude, kurtosis, max dB level, average dB level, and/or noiseexposure (e.g., dosage) and reporting of noise exposure as it pertainsto several existing (or future models) such as the AHAAH.

Those of skill in art will readily appreciate that an audio recordingsystem could also be mounted in a housing with a different size, shape,and/or weight. The housing could also be made of a different material(e.g., stamped pieces of metal) or omitted altogether. For instance, thesystem components could be stitched into or onto an article of clothing,such as a jacket or shirt, or into or onto a bag, web gear, or any othersuitable article with or without the microphones. The system could alsobe mounted on or in a portable device, vehicle (e.g., inside an aircraftcabin, racecar, construction vehicle, etc.), or in a particular location(e.g., a shop floor). One or more system components may be wearableand/or implantable.

FIG. 13 is a diagram illustrating a high fidelity noise exposurerecording system in accordance with some embodiments. The recordingsystem may be designed for mounting on a user's head, attaching to, forexample, the neck, ear, etc. The recording system includes a microphone(e.g., a 0.125-inch microphone) and an accelerometer. The recordingsystem includes at least one circuit board, such as an analog board forregulating power and/or A/D conversion; an FPGA/MCU board forcontrolling onboard processing, altimeter measurements, GPScommunications, temperature sensing, humidity measurements, and/or IMUmeasurement; and a radio board for controlling at least onecommunication interface (e.g., Bluetooth®, antenna, etc.), a datastorage device, and/or a power source (e.g., a battery or rechargeablebattery). Together, the power source and the at least one circuit boardmay be situated in an electronics housing featuring heat sinks, anaccess opening (e.g., an easily slidable opening), microphoneconnectors, and/or USB connections.

Although Bluetooth® communication is widely used in the commercialsector for communication due to its ease of use and range, this methodis unacceptable in a tactical environment because it is easy to detect.In some embodiments, a dosimeter has the capability to use either aBluetooth® radio or to include a wideband or narrowband tunable radioonboard with a defined power level and range. The latter radio allowsthe system to communicate data in a tactical, and potentially covert,manner. The integrated radio (e.g., Bluetooth® or tunable narrow band)may be used to link several systems to form a real-time network.

FIG. 14 is a diagram illustrating dosimeter components including a USBconnector to connect with, for example, a phone (e.g., a smartphone) inaccordance with some embodiments. The USB may be connected to the phonevia a (e.g., flexible) cable. The dosimeter components in FIG. 14 alsoinclude a tunable wideband or narrowband radio onboard with a definedpower level and range to communicate data in a tactical, and potentiallycovert, manner in accordance with some embodiments.

The second-generation noise dosimeter was aimed at improving the signalquality above that of the original version, through modifications toboth the internal circuitry and the microphones (left and right). Thegoal for this device was to collect on-body, laboratory-grademeasurements that meet or are close to instrumentation requirementsspecified in MIL-STD-1474E, while maintaining a suitable form factor. Inaddition, several auxiliary sensors are integrated into the device tocapture GPS data, temperature, barometric pressure, and acceleration.The accelerometer is particularly valuable for its ability to identifyphysical impacts that produce false impulse-like signals on themicrophone.

FIG. 15 is an image illustrating the second-generation prototype inaccordance with some embodiments. A GRAS 47DX ⅛″ pressure microphone wasselected for the microphone in the second-generation dosimeter designdue to its form factor (suitable for near-ear placement), its largemeasurement range (up to 185 dB), and its frequency response (up to 100kHz). Although ideal for research studies, one downside to thismicrophone is the cost, which might prevent wide-scale adoption foroccupational and military noise measurements. However, some embodimentsmay be built with less costly microphones that have sufficient signalbandwidth and quality. In some embodiments, the product size may bereduced while battery life is increased to enable extended periods ofuse (e.g., weeks to months). For example, the sample rate has a strongeffect on the power consumed by the analog-to-digital converter, butalso on any processing that must be done on the resulting digital data.The analog-to-digital converter used in the device, which has 128 kHzsampling frequency and a 285 mW power consumption, was selected forbeing a midpoint in the trade-off between SWaP and data quality.

To verify the accuracy of the MIT LL second-generation dosimeter,simultaneous laboratory measurements were made with the dosimeter and areference data-acquisition system using two GRAS 47DX microphonesco-located near the ear of an acoustic test fixture. FIG. 16 is a seriesof plots illustrating pressure waveforms (top) and ⅓-octave-band spectra(bottom) for the MIT LL 2nd-generation noise dosimeter and a referencedata-acquisition system collected with matching, co-located microphonesin accordance with some embodiments. There is a good correspondencebetween the two systems. FIG. 16 contains an example of the datacollected for a 161-dB peak SPL impulse noise generated from acompressed-air shock tube. The second generation dosimeter shows goodcorrespondence to the reference system, a 24-bit National Instrumentslaboratory-grade system that samples at 200 kHz. The dosimeter measureda peak SPL of 161.0 dB and an L_(Aeq,100ms) of 136.7 dB, while thereference system measured a peak SPL of 161.3 dB and an L_(Aeq,100ms) of137.2 dB. The median difference in the ⅓-octave-band levels was 0.7 dB.

Another important consideration in the dosimeter design is the choice ofdamage risk metrics to be output by the device, ideally computed inreal-time. Since the second-generation prototype is designed forresearch and the damage risk metrics have not yet been settled on, theprototype is typically configured as a sound recorder and evaluation ofnoise exposure metrics is performed off-line. The prototypes are,however, capable of on-board data processing via a Xylinx Zynq, whichincludes both an FPGA and dual-core ARM processor. A hybrid approach forstorage of continuous and impulse noise may be used to reduce the datastorage requirements (e.g., about 3 GB per hour for stereo recording) onthe tactical noise dosimeter while preserving select time-pressureintervals for further analysis. In this hybrid approach, data are storedin two output streams on different time-scales. Average A-weightedlevels or octave bands of the background noise levels are captured on arelatively slow, uniformly sampled time scale. Simultaneously, impulsesthat exceed a threshold are detected and stored as full pressurewaveforms for offline analysis since impulse metrics are less agreedupon by the hearing community. This technique reduces the data storagerequirements while still capturing significantly more information than aCOTS noise dosimeter. Currently, the prototype dosimeter has 128 GB ofavailable storage through a microSD card.

FIG. 17 is a diagram illustrating a signal processing and data storagealgorithm for a complex-noise, tactical noise dosimeter in accordancewith some embodiments. Output 1 consists of background noise levelmeasurements (e.g. sound level or octave band measurements at 1 sintervals). Output 2 consists of the full pressure waveform when animpulse noise event is detected. This technique allows for applicationof existing and future damage risk metrics to be applied specificallyfor impulse noise, while reducing the data storage requirements, inaccordance with some embodiments.

Lessons learned from field collections with COTS recorders and thefirst-generation dosimeter also have helped inform the design of thesecond generation dosimeter package. The interface of the device has nosettings that are exposed in order to limit opportunities for humanerror. A display to indicate the status of the device (verifyfunctionality and system health) will be added for identifying deviceconcerns during a fielding. Another challenge with on-body dosimetry isartifacts due to acceleration effects or touching the microphone.Knocking artifacts recorded in an acoustic waveform are very similar toimpulsive noise, but since they are not representative of the noisetransmitted to the ear drum, they can result in gross over-estimates ofthe noise exposure if they are integrated into personal dose estimates.In the Afghanistan fielding of helmet-mounted noise dosimeters, droppedhelmets and other impacts associated with military operations produced alarge number of artifacts in the data that could not be automaticallyscreened from the dosage calculations. Microphone design and diaphragmsize can have a strong effect on acceleration sensitivity, which istypically maximal in the direction of diaphragm motion. Hearing aid andMEMS microphones typically have low acceleration sensitivity and areideal for on-body acoustical recording, but do not span the dynamicrange for military noise exposures. Piezoelectric microphones may alsobe a good option, but are often less sensitive in the range of humanacoustic sensitivity. The MIT LL second-generation dosimeter has beendesigned with co-located accelerometers at each microphone and in thedevice enclosure. These additional sensors will be used to help detectand remove microphone knocking through on-board processing describedabove in accordance with some embodiments.

In 2013, several Marines in Afghanistan were outfitted with the MIT LLfirst-generation on-body dosimeters in a study conducted by MERS andUSARIEM Noise exposure was measured for approximately 12 hours each dayover a period of two days, providing samples of the operational noiseenvironment. Participants received a briefing on the study and wereoutfitted with either an MIT LL helmet sensor or a COTS TASCAM DR-05recorder. Ten of each device were available for the fielding. On eachday, two platoons completed their daily patrol with the devicesrecording. The Marines transitioned between being mounted in vehiclesand walking in the vicinity while on patrol. None of the Marines worehearing protection.

FIG. 18 is a series of simultaneous recordings of live weapons fire asobserved by two Marines with on-body dosimeters in accordance with someembodiments. In FIG. 18, the data was recorded by the sensors for twoMarines in close proximity to each other during a firefight. Threedifferent combatants can be heard firing in the recording, where theexposure for Marine 1 is highest during the last section, and in thefirst section for Marine 2. These data demonstrate the complicated noisedosage that accumulates based on the position relative to the impulsenoise source, and thus the need for on-body sensors to make personalizedmeasurements.

Damage risk metrics such as those listed in TABLE 1 have been calculatedfrom the recorded data. One challenge that arose in evaluating the dosefrom the Afghanistan collection is that knocking artifacts frequentlyoccurred from the motion of the Marines and incidental contact with themicrophones. Under these circumstances, directly integrating theA-weighted energy over the full 12 h recordings would result in inflateddose values since much of the energy comes from the knocking artifacts.To avoid this, a number of artifact-free intervals were manuallyidentified and analyzed. TABLE 3 shows the damage risk metrics for theshort interval shown in FIG. 18.

TABLE 3 L′_(Aeq, 8 h) Peak w/ AHAAH AHAAH SPL L_(Aeq, 8 h) KurtosisL_(IAeq, 8 h) Unwarned Warned (dB) (dBA) dBA) (dBA) (ARU) (ARU) AnalysisImpulse Criterion Location Interval Count ≤140 ≤85 ≤85 ≤85 ≤200/500≤200/500 Afghanistan: 6 sec 19 152 85 92 76 2699 463 Marine 1Afghanistan: 6 sec 19 150 84 91 78 1596 140 Marine 2 Carrier: 24 h 0 12375 76 n/a n/a n/a Room 1 Carrier: 24 h 0 122 79 81 n/a n/a n/a Room 2Carrier: 24 h 24 143 81 84 58 1800 695 Room 3

During this short 6 second interval, both Marines are exposed toequivalent noise levels L_(Aeq,8h) near the recommended daily limit of85 dBA. Peak levels observed from the shots fired nearest to them exceedthe recommended limit of 140 dB. The MIL-STD-1474E impulse metrics,L_(I Aeq,8h) and AHAAH ARU, are only calculated on the impulses andexclude background energy. The corrected L_(I Aeq,8h) metric yields avalue several dB lower than the conventional L_(Aeq,8h). Although theimpulses in this data were due to small arms fire, the A-durationestimation resulted in correction factors that reduced the energy ofeach impulse by several dB (ranging from 4 to 16.5 dB for the 19 shotsshown). The last metrics shown in the table are AHAAH ARU calculated forthe Unwarned (middle ear reflex not active prior to the arrival of eachimpulse) and Warned (middle ear reflex active prior to the arrival ofeach impulse) states. In the Unwarned state, the 500 ARU limit foroccasional exposure is significantly exceeded. However, assuming the earis in the Warned state, which may be a reasonable assumption for asoldier firing his or her own weapon, the ARU falls slightly below thelimit.

Other prominent noise sources in military environments include ground,air, and sea-based vehicles. In particular, aircraft carriers are amongthe loudest of military environments; above deck, personnel wear doublehearing protection to protect against the extreme noise levels from jetsas they launch and land on the carrier. However, the noise levels arehigh even below deck, with a complex mix of continuous, intermittent,and impulsive noise events from many contributing sources.

FIG. 19 is a plot illustrating an example of the 24-hour dosimetercollection on an aircraft carrier showing high intermittent/impulsivenoise in a living space at the front of the ship during flightoperations and lower (but still moderately high) levels after flightoperations conclude in accordance with some embodiments. The data wascollected in a study funded by the Office of Naval Research (ONR) incollaboration with Noise Control Engineering LLC during a Joint StrikeFighter (JSF) exercise. Noise was recorded throughout the seven-dayexercise using stationary, free-field COTS sensors (TASCAM DR-40recorders). Significant background levels were observed in the livingspaces below deck, and persistent recordings show shifts in thecontinuous background levels that range from 55 dBA (the noise floor ofthe recorder) up to 62 dBA. The elevated background levels correspond totime periods leading up to flight operations and likely are associatedwith high loads on support machinery in use. Prior to launch, thefighter jets engage engines at full power, producing intermittent burstsof high-intensity noise that last for 20 to 30 seconds. Finally,occasional impulses are observed in the data, associated with thecatapult brake that produces a sharp impact noise that reverberatesthrough the ship as each aircraft is launched.

TWA noise levels are represented in FIG. 19 as an 8-hour moving averageof the noise measurements (solid red line). The average level in thisliving space during flight operations is just above 80 dB. Forcomparison, the Navy guidance for 8-hour TWA noise levels where singleand double hearing protection devices (HPDs) are required are shown asdashed lines. The upper-most dashed line represents the noise reductionrating for the best double HPD approved by the DoD.

This 24-hour measurement shows that noise in the living quarters belowdeck of an aircraft carrier reaches very high levels during flightoperations and the noise continues at moderately high levels even afterflight operations conclude. These 24/7 noise conditions may not supportfull TTS recovery each day. Further on-body and in-ear dosimetermeasurements along with audiometric data are needed to better understandthis issue, as the accumulated risk of hearing damage might besignificantly greater when exposures from the flight-deck are included.

FIG. 20 is a scatter plot of example damage risk metrics calculated fromthe noise recordings in living spaces below deck in accordance with someembodiments. Each point represents the A-weighted noise level for eachlaunch plotted against the corresponding kurtosis correction based onthe approach described by Goley et al. (2011), calculated during a 1second interval of an aircraft launch from the catapult overhead. Rooms1 and 2 are located below the jet blast deflectors, so the primary noiseexposure is from intermittent, continuous-noise jet engine blasts.Furthermore, the launch energy is not highly impulsive, so thekurtosis-correction factor is generally small in these rooms. Room 3,located at the front of the ship in close proximity to the breakingmechanism for the catapults, contains the loudest noise during launches,as indicated by the higher L_(Aeq,8h) values. Additionally, the noise inthis room is highly impulsive, as seen in the example waveform in theinset of FIG. 20, leading to relatively large kurtosis-based correctionfactors for most of the launches in this room.

TABLE 3 also summarizes the metrics for the three aircraft carrier roomsaccumulated over a 24 h period where 22 aircraft were launched from thecatapults overhead. Since Rooms 1 and 2 did not contain high-levelimpulses, the impulse metrics L_(Aeq,8h) and AHAAH ARU are notappropriate metrics for these rooms, but the L_(Aeq,8h) characterizesthe continuous and intermittent noise energy accumulated throughout theday. The L_(Aeq,8h) is below the recommended 85 dBA limit, butconsidering that they are living spaces, the 75-79 dB levels may notprovide adequate recovery conditions for personnel during their off-dutyhours. Applying a kurtosis correction increases the equivalent noiselevels by 1-2 dB. In Room 3 the impulse peak levels are much higher,reaching the 140 dB peak limit for most launches. For this room theimpulse metrics L_(Aeq,8h) and AHAAH ARU are calculated for high-levelpeaks as well as the conventional L_(Aeq,8h) integrated over the full 24hour period. The damage risk metrics give conflicting results: both theconventional and kurtosis-corrected L_(Aeq,8h) are close to, but belowthe recommended 85 dBA limit. The impulse metric L_(Aeq,8h) whichintegrates over the 100 ms window for each impulse (neglecting allintermittent and continuous background noise) yields a very low hazard.The value is particularly low because A-duration calculations are notwell-suited for the highly-reverberant impact noise observed in thisroom. Due to the reverberation, A-durations calculated for theseimpulses are typically longer than 2.5 ms, resulting in the maximumreduction of 16.5 dB in the L_(Aeq,8h) risk metric. Conversely, theAHAAH ARU metric predicts extreme hazard from the impulses in this roomfor both Warned and Unwarned states. The inconsistencies seen betweenL_(Aeq,8h), L_(I Aeq,8h,) and AHAAH ARU damage risk metrics for thisroom, as well as the uncertainty in when to consider Warned versusUnwarned AHAAH ARU in this noise environment, emphasize the need forfurther research to understand the limitations of damage metrics anddevelop clearer guidelines for which metric or metrics should be used ina scenario.

With decades of investment in noise assessments, the military hasextensive recordings from stationary measurement systems collected onships, ground vehicles, aircraft and other relevant noise environments.In a diffuse, continuous sound field it is possible to leverage existingmeasurements or acoustic models of a noise environment such as a Navyship and generate representative free-field noise metrics for a specificroom or location. While these free-field noise metrics provide valuableinformation about noise conditions throughout the ship, they fall shortof estimating the individual exposure of a crew member, since personnelmove throughout the ship over the course of a day and the exposure of anindividual is unique based on his or her sequence of activities.Estimating a dose for a given individual relies on layers of assumptionsabout personnel movement above and below deck over a 24 hour period aswell as when crew are wearing hearing protection devices (HPDs).Similarly, while noise level recordings of individual weapons orvehicles are readily available, there are no noise exposure collectionsduring dismounted combat operations.

Uncertainties associated with estimating personnel movement as theyperform their duties may be avoided with on-body dosimetry where thelocal noise conditions are directly sampled by the device in accordancewith some embodiments. On-body measurements may also be valuable for thepurpose of developing task-based transfer functions which could be usedto translate extensive collections of free-field military environmentmeasurements into representative dosage for a given task.

Another level of fidelity needed to capture the dose experienced by anindividual is to relate the noise measured at a position on the body tothe noise arriving in the ear canal. This requires a transfer functionto account for spatial, spectral, and temporal filtering of the noise bythe torso, head and outer ear. While the top of the shoulder ishistorically considered an optimal position for a dosimeter microphone,the differences in sound pressure among on-body locations can vary up to15 dB, which could impact temporary and permanent threshold shiftssignificantly. This problem is even more relevant for military andimpulse noise, where head and helmet shadowing and pinna resonances canstrongly effect the high-frequency content of the energy delivered tothe eardrum.

According to some embodiments, in-ear dosimetry eliminates the need foron-body to in-ear transfer function. Furthermore, a microphonepositioned in the ear can directly measure noise exposure fromheadphones as well as noise suppression from HPDs. FIG. 21 is a diagramillustrating how the placement of the microphone for dosimetermeasurements affects the process used in assessing risk of NIHL inaccordance with some embodiments. The ideal microphone placement forpersonal dosimetry is in the ear since it requires very few assumptionsabout the activities of the individual. However, in-ear dosimetry mayinterfere with standard hearing protection, situational awareness,comfort, and cause occlusion making it not practical for 24 hourdosimetry or long-term use. Near-ear dosimetry can provide a goodcompromise of fidelity and practical use in fieldings.

To illustrate the differences between the various different microphoneplacements, a laboratory test was performed to record a series ofimpulses from four microphone positions simultaneously, as shown in FIG.22, in accordance with some embodiments. FIG. 22 is a series of plotscomparing peak SPL (upper right) and AHAAH model (lower right) at 4recording locations for impulse noise in accordance with someembodiments.

The setup used a GRAS 45CB acoustic test fixture to obtain in-earmeasurements; the “near-ear” dosimeter microphone was mounted on thetest fixture as shown in FIG. 15. All of the pressure microphones wereoriented so that the sound-pressure wave from an impulsive source was ata 90-degree angle of incidence to the diaphragm, so noangle-of-incidence correction is required.

Since all of recordings were made simultaneously for each impulse, thedamage risk to the inner ear is intrinsically identical regardless ofthe measurement location. For a frontal impulse noise source (0-degreeazimuth), the free-field, on-body and near-ear microphones all producesimilar peak and A-weighted sound-pressure levels, but the in-earmeasurement is amplified by nearly 10 dB due to the outer-ear and pinna.Current exposure metrics, such as L_(Aeq,8h), are based on free-fieldmeasurements, because they are more convenient to obtain with asound-level meter. However, an in-ear measurement may be a more accuratepredictor of hearing damage than a free-field measurement, even if it ismore difficult to measure in practice.

As previously mentioned in Section 2.2, the AHAAH model providestransfer functions, that include various assumptions, to correct formicrophone placement. The location options supported by AHAAH are (1)free-field, (2) ear-canal entrance, or (3) eardrum, and were appliedappropriately to the data shown in FIG. 22. For a free-fieldmeasurement, the AHAAH model assumes that the energy is transmitteddirectly to the ear (i.e., that the measurement location and the headare in the same place and that the sound source is aligned in azimuthand elevation with the ear canal), providing a worst case estimate ofthe transmission. Due to this assumption, the reported ARU value washighest for this sample dataset when calculated using free-field-likeconditions. If the ARU in-ear value is taken as ground truth, the errorin predicted risk increases as the distance of the measurement from theear increases.

This experiment was conducted for a single source angle (frontal), butrelationships between the microphone measurements will depend on thelocation of the source as well as placement of the on-body microphone.This issue of on-body microphone placement is not accounted for in thecurrent ASA and ANSI Standard 51.25 specification for personal noisedosimeters (ANSI, 1991), and may be even more important when consideringimpulsive or complex noise environments. Finally, metrics should beadapted for in-ear dosimetry when combined with hearing protectiondevices. In the most recent version of the AHAAH model, as described inMIL-STD-1474E, a hearing protector simulator is included to betterestimate exposure at the ear drum.

The prevalence of NIHL in the military has continued to increase overthe past decade, even as Department of Defense efforts to protect andconserve hearing have increased. A key step in developing strategies toreduce NIHL is to improve the ability to measure noise exposure for theindividual and to predict the risk of hearing injury accurately. The MITLL second-generation dosimeter prototype is in development to helpbridge the gap between COTS dosimeters that provide persistent on-bodynoise exposure measurements for industrial environments, and large-SWaPlaboratory-grade sound-pressure meters capable of measuring the extremelevels and broadband characteristics that may be encountered in militarynoise environments.

Translating noise exposure to auditory damage through appropriatemetrics is still an open area of research. Progress in this area hasbeen slow due to the very few data sets that contain both noise exposureand audiometric data for humans. Proposed near-term collections with theMIT LL dosimeter prototype include Marine training exercises with livefire and blasts as engineering tests of the system. Future collectionsalso may include coordinated audiometry and potentially otherphysiological data such as genetic biomarkers. Collections of this typewill support the continued validation of proposed damage risk metricsand development of more comprehensive modeling of auditory damage fromnoise. Finally, it is important to note that a potential future use forpersonalized dosimetry relates to recent studies that show promisingresults for reducing NIHL with therapeutic agents. When administeredwithin one hour of the exposure, pharmacological interventions mayprovide as much as 30 dB of protection against a permanent thresholdshift. On-body noise dosimetry may be used to provide an alert tosoldiers and medics when a noise exposure exceeds a dangerous threshold.This immediate feedback could improve the chances of delivering therapyto individuals who need it during the short window of opportunity inwhich it would be most effective.

CONCLUSION

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

The above-described embodiments can be implemented in any of numerousways. For example, embodiments of designing and making the analog and/ordigital circuitry elements disclosed herein may be implemented usinghardware, software or a combination thereof. When implemented insoftware, the software code can be executed on any suitable processor orcollection of processors, whether provided in a single computer ordistributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a PersonalDigital Assistant (PDA), a smart phone or any other suitable portable orfixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks, wired networks or fiber optic networks.

The various methods or processes (e.g., of designing and making theanalog and/or digital circuitry disclosed above) outlined herein may becoded as software that is executable on one or more processors thatemploy any one of a variety of operating systems or platforms.Additionally, such software may be written using any of a number ofsuitable programming languages and/or programming or scripting tools,and also may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

In this respect, various inventive concepts may be embodied as acomputer readable storage medium (or multiple computer readable storagemedia) (e.g., a computer memory, one or more floppy discs, compactdiscs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other non-transitory medium or tangible computer storagemedium) encoded with one or more programs that, when executed on one ormore computers or other processors, perform methods that implement thevarious embodiments of the invention discussed above. The computerreadable medium or media can be transportable, such that the program orprograms stored thereon can be loaded onto one or more differentcomputers or other processors to implement various aspects of thepresent invention as discussed above.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of embodiments as discussedabove. Additionally, it should be appreciated that according to oneaspect, one or more computer programs that when executed perform methodsof the present invention need not reside on a single computer orprocessor, but may be distributed in a modular fashion amongst a numberof different computers or processors to implement various aspects of thepresent invention.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Also, various inventive concepts may be embodied as one or more methods,of which an example has been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety,including:

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All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of” “only one of” or“exactly one of” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

1. A system for continuously collecting sound pressure due to bothcontinuous noise and impulse noise in an environment, the systemcomprising: a first sensor to obtain a first analog signalrepresentative of impulse noise sound pressure in a first amplituderange from about 100 dB to about 180 dB; a second sensor to obtain asecond analog signal representative of continuous noise sound pressurein a second amplitude range from about 20 dB to about 140 dB, the secondamplitude range being different from the first amplitude range; at leastone analog-to-digital converter (ADC), operably coupled to the firstsensor and the second sensor, to sample: the first analog signal at afirst sampling rate equal to or greater than twice the reciprocal of aminimum impulse noise rise time in the first analog signal, therebygenerating a first digital signal; and the second analog signal at asecond sampling rate equal to or greater than twice the reciprocal of aminimum impulse noise rise time in the second analog signal, therebygenerating a second digital signal; a first accelerometer to obtain afirst accelerometry signal representative of accelerometry data in closeproximity to the first sensor and the second sensor; and a secondaccelerometer to obtain at least one second accelerometry signalrepresentative of accelerometry data remote from the at least one firstsensor and the at least one second sensor relative to the at least onefirst accelerometer, at least one processor including: a first combiningnode to combine the first digital signal and the second digital signalinto a combined audio signal having a combined amplitude range, thecombined amplitude range being larger than the first amplitude range,larger than the second amplitude range, and less than or about equal tothe sum of the first amplitude range and the second amplitude range, thecombined audio signal representing both the continuous noise and theimpulse noise in the environment; a shock-artifact detection filter toidentify a time frame potentially including a shock artifact based atleast in part on the first accelerometry signal and the secondaccelerometry signal; a frequency filter to generate abackground-removed audio signal by removing background noise from thecombined audio signal; an adaptive filter to estimate the shock artifactbased at least in part on the identified time frame and thebackground-removed audio signal; and a second combining node to producea shock-artifact-free audio signal by subtracting the estimated shockartifact from the combined audio signal.
 2. The system of claim 1,wherein the system is configured to be worn by a subject.
 3. The systemof claim 2, wherein: the first sensor, the second sensor, and the firstaccelerometer are configured to be worn on the subject's head; and thesecond accelerometer is configured to be worn on the subject's torso. 4.The system of claim 3, further comprising: a third sensor to obtain athird analog signal representative of impulse noise sound pressure inthe first amplitude range; a fourth sensor to obtain a fourth analogsignal representative of continuous noise sound pressure in the secondamplitude range; and a third accelerometer to obtain a thirdaccelerometry signal representative of accelerometry data in closeproximity to the third sensor and the fourth sensor, wherein: the firstsensor, the second sensor, and the first accelerometer are configured tobe positioned on a first side of the subject's head; and the thirdsensor, the fourth sensor, and the third accelerometer are configured tobe positioned on a second side of the subject's head, the second sideopposite the first side.
 5. The system of claim 1, wherein the firstaccelerometer is a triaxial accelerometer.
 6. The system of claim 1,wherein the second accelerometer is a nine-channel accelerometer.
 7. Thesystem of claim 1, further comprising at least one memory device forstoring a representation of the shock-artifact-free audio signal.
 8. Thesystem of claim 1, wherein: the first amplitude range is from about 115dB to about 180 dB; the second amplitude range is from about 75 dB toabout 140 dB; and the combined amplitude range is less than about 105dB.
 9. A system for continuously collecting sound pressure due to bothcontinuous noise and impulse noise in an environment, the systemcomprising: at least one processor, operably coupled to: a first sensorto obtain a first audio signal representative of impulse noise soundpressure in a first amplitude range from about 100 dB to about 180 dB,sampled at a first sampling rate equal to or greater than twice thereciprocal of a minimum impulse noise rise time in the impulse noisesound pressure; a second sensor to obtain a second audio signalrepresentative of continuous noise sound pressure in a second amplituderange from about 20 dB to about 140 dB, sampled at a second samplingrate equal to or greater than twice the reciprocal of a minimum impulsenoise rise time in the continuous noise sound pressure, the secondamplitude range being different from the first amplitude range; a firstaccelerometer to obtain a first accelerometry signal representative ofaccelerometry data, wherein the at least one processor includes: a firstcombining node to combine the first audio signal and the second audiosignal into a combined audio signal having a combined amplitude range,the combined amplitude range being larger than the first amplituderange, larger than the second amplitude range, and less than or aboutequal to the sum of the first amplitude range and the second amplituderange, the combined audio signal representing both the continuous noiseand the impulse noise in the environment; a shock-artifact detectionfilter to identify a time frame potentially including a shock artifactbased at least in part on the first accelerometry signal; a frequencyfilter to generate a background-removed audio signal by removingbackground noise from the combined audio signal; an adaptive filter toestimate the shock artifact based at least in part on the identifiedtime frame and the background-removed audio signal; and a secondcombining node to produce a shock-artifact-free audio signal bysubtracting the estimated shock artifact from the combined audio signal.10. The system of claim 9, wherein the first accelerometer is a triaxialaccelerometer.
 11. The system of claim 9, further comprising at leastone memory device for storing a representation of theshock-artifact-free audio signal.
 12. The system of claim 9, furthercomprising at least one communication interface for transmitting arepresentation of the shock-artifact-free audio signal.
 13. The systemof claim 9, wherein: the first amplitude range is from about 115 dB toabout 180 dB; the second amplitude range is from about 75 dB to about140 dB; and the combined amplitude range is less than about 105 dB. 14.A method for continuously collecting sound pressure due to bothcontinuous noise and impulse noise in an environment, the methodcomprising: obtaining a first audio signal representative of impulsenoise sound pressure in a first amplitude range from about 100 dB toabout 180 dB, sampled at a first sampling rate equal to or greater thantwice the reciprocal of a minimum impulse noise rise time in the impulsenoise sound pressure; obtaining a second sensor to obtain a second audiosignal representative of continuous noise sound pressure in a secondamplitude range from about 20 dB to about 140 dB, sampled at a secondsampling rate equal to or greater than twice the reciprocal of a minimumimpulse noise rise time in the continuous noise sound pressure, thesecond amplitude range being different from the first amplitude range;obtaining a first accelerometry signal representative of accelerometrydata; combining the first audio signal and the second audio signal intoa combined audio signal having a combined amplitude range, the combinedamplitude range being larger than the first amplitude range, larger thanthe second amplitude range, and less than or about equal to the sum ofthe first amplitude range and the second amplitude range, the combinedaudio signal representing both the continuous noise and the impulsenoise in the environment; detecting a time frame potentially including ashock artifact based at least in part on the first accelerometry signal;generating a background-removed audio signal by removing backgroundnoise from the combined audio signal with a frequency filter; applyingan adaptive filter to estimate the shock artifact based at least in parton the identified time frame and the background-removed audio signal;and subtracting the estimated shock artifact from the combined audiosignal to produce a shock-artifact-free audio signal.
 15. The method ofclaim 14, wherein: the first amplitude range is from about 115 dB toabout 180 dB; the second amplitude range is from about 75 dB to about140 dB; and the combined amplitude range is less than about 105 dB. 16.A system for continuously collecting sound pressure in an environment,the system comprising: a first sensor with a first sensitivity to obtaina first analog signal representative of sound pressure levels; a secondsensor with a second sensitivity to obtain a second analog signalrepresentative of sound pressure levels, the second sensitivity beinglower than the first sensitivity; at least one analog-to-digitalconverter (ADC), operably coupled to the first sensor and the secondsensor, to separately sample the first analog signal and the secondanalog signal, thereby generating a first digital signal and a seconddigital signal; at least one processor, operably coupled to the at leastone ADC, to: perform gain matching between the first digital signal andthe second digital signal to estimate gain; and combine the firstdigital signal and the second digital signal into a combined audiosignal by at least one of: multiplexing between the second digitalsignal and a product of the first digital signal and the estimated gainbased on signal amplitude; and generating a weighted sum of the firstdigital signal and the second digital signal based on signal-to-noiseratio.
 17. The system of claim 16, wherein at least one of the firstsensor and the second sensor is at least one of a microphone and apiezoelectric pressure sensor.
 18. The system of claim 16, wherein thegain matching is performed using a least square solution to${\underset{A}{\arg \; \min}\left( {{g\lbrack n\rbrack} - {{Af}\lbrack n\rbrack}} \right)},$wherein A is optimal gain, f is the first digital signal in the timedomain, and g is the second digital signal in the time domain.
 19. Amethod for continuously collecting sound pressure in an environment, themethod comprising: obtaining a first analog signal representative ofsound pressure levels from a first sensor with a first sensitivity;obtaining a second analog signal representative of sound pressure levelsfrom a second sensor with a second sensitivity, the second sensitivitybeing lower than the first sensitivity; separately sampling the firstanalog signal and the second analog signal, using at least oneanalog-to-digital converter (ADC), thereby generating a first digitalsignal and a second digital signal; performing gain matching between thefirst digital signal and the second digital signal to estimate gain; andcombining the first digital signal and the second digital signal into acombined audio signal by at least one of: multiplexing between thesecond digital signal and a product of the first digital signal and theestimated gain based on signal amplitude; and generating a weighted sumof the first digital signal and the second digital signal based onsignal-to-noise ratio.